Systems and methods for x-ray imaging

ABSTRACT

The present disclosure provides a system for imaging via an imaging device including a plurality of radiation sources. Each of at least a portion of the plurality of radiation sources may be configured with a beam stop array that is configured to block at least a portion of radiation beams emitted by the radiation source. For one of the at least a portion of the plurality of radiation sources, the system may determine, based on a scatter distribution, first image data, and second image data, third image data of a subject corresponding to each of the at least a portion of the plurality of radiation sources. For each of at least a portion of the plurality of radiation sources, the system may further determine, based on image data of the subject, target image data of the subject using a calibration model.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Continuation of International Application No.PCT/CN2021/080940 filed on Mar. 16, 2021, which claims priority ofChinese Patent Application No. 202010575955.5 filed on Jun. 22, 2020,and Chinese Patent Application No. 202011185982.8 filed on Oct. 29,2020, the contents of each of which are hereby incorporated byreference.

TECHNICAL FIELD

The disclosure generally relates to X-ray imaging systems, and moreparticularly relates to systems and methods for imaging using an X-rayimaging device including a plurality of radiation sources.

BACKGROUND

X-rays have been widely used in medical diagnosis, radiotherapyplanning, surgery planning, radiotherapy, and other medical procedures.In some embodiments, using an X-ray imaging technique, one singleradiation source may move or rotate in the acquisition of imaging datato acquire the imaging data under different angles of the singleradiation source, which increases scanning time. And a motion artifactcaused by, e.g., motion of the radiation source, time delay caused by athermionic emission mechanism of the radiation source, etc., may reducea spatial resolution of the imaging data, thereby decreasing the imagequality. A planar array imaging technique may avoid the motion of thesignal radiation source, thereby greatly reducing the motion artifact inimaging data. However, using the planar array imaging technique, imagedegradation may be caused by scattering. In addition, geometricparameters of an imaging device may change caused by mechanical accuracyerrors during the service of the imaging device, which causes poor imagequality. Therefore, it is desired to provide systems and methods forimaging for the planar array imaging technique with improved efficiencyand accuracy.

SUMMARY

According to a first aspect of the present disclosure, a system forimaging via an imaging device including a plurality of radiation sourcesis provided. Each of at least a portion of the plurality of radiationsources may be configured with a beam stop array that is configured toblock at least a portion of radiation beams emitted by the radiationsource. The system may include at least one storage device storingexecutable instructions, and at least one processor in communicationwith the at least one storage device. When executing the executableinstructions, the at least one processor may cause the system to performone or more of the following operations. For one of the at least aportion of the plurality of radiation sources, the operations mayinclude obtaining first image data of a subject acquired by the imagingdevice when the beam stop array is arranged on a path of radiation beamsemitted by the radiation source. For one of the at least a portion ofthe plurality of radiation sources, the operations may include obtainingsecond image data of the subject acquired by the imaging device when thebeam stop array is not arranged on the path of radiation beams emittedby the radiation source. For one of the at least a portion of theplurality of radiation sources, the operations may also includedetermining, based on the first image data, a scatter distributionassociated with the subject included in the second image data. For oneof the at least a portion of the plurality of radiation sources, theoperations may further include determining, based on the scatterdistribution and the second image data, third image data of the subjectcorresponding to each of the at least a portion of the plurality ofradiation sources.

In some embodiments, the imaging device may include a digital breasttomosynthesis (DBT) device.

In some embodiments, the beam stop array may include a support andmultiple elements. Each of multiple elements may include a material withan attenuation coefficient exceeding an attenuation coefficient of amaterial of the support.

In some embodiments, the determining, based on the first image data, ascatter distribution associated with the subject may include performingan interpolation operation on the first image data to obtain the scatterdistribution.

In some embodiments, a radiation dose of the first image data may beless than a radiation dose of the second image data.

In some embodiments, the determining, based on the scatter distributionand the second image data, third image data of the subject correspondingto each of the at least a portion of the plurality of radiation sourcesradiation source may include determining a ratio of the radiation doseof the second image data and the radiation dose of the first image data;and determining, based on the ratio, the scatter distribution, and thesecond image data, the third image data.

In some embodiments, the plurality of radiation sources may include atarget portion in which each radiation source is not configured with abeam stop array. The operations may include, for a radiation source inthe target portion, obtaining fourth image data of the subject acquiredby the imaging device via scanning the subject. The operations mayinclude, for a radiation source in the target portion, determining anestimated scanner distribution included in the fourth image data basedon one or more scatter distributions that are determined based on thefirst image data corresponding to one or more reference radiationsources. The operations may further include, for a radiation source inthe target portion, determining, based on the fourth image data and thescanner distribution, third image data of the subject corresponding tothe radiation source in the target portion.

In some embodiments, the determining an estimated scanner distributionincluded in the fourth image data may include determining the estimatedscanner distribution included in the fourth image data by performing aninterpolation operation on the one or more scatter distributions.

In some embodiments, the operations further include determining, basedon the third image data of the subject corresponding to each of theplurality of radiation sources, target image data of the subject.

According to a second aspect of the present disclosure, a system forimaging via an imaging device including a plurality of radiation sourcesis provided. Each of at least a portion of the plurality of radiationsources may be configured with a beam stop array that is configured toblock at least a portion of radiation beams emitted by the radiationsource. The system may include at least one storage device storingexecutable instructions, and at least one processor in communicationwith the at least one storage device. When executing the executableinstructions, the at least one processor may cause the system to performone or more of the following operations. For one of the plurality ofradiation sources, the operations may include obtaining image data ofthe subject acquired by the imaging device via scanning the subjectbased on radiation beams emitted by the radiation source. The image datamay include scatter data caused by a scattering of at least a portion ofthe radiation beams passing through the subject. For one of theplurality of radiation sources, the operations may also includeobtaining a trained machine learning model. For one of the plurality ofradiation sources, the operations may further include determining, basedon the trained machine learning model and the image data, target imagedata of the subject corresponding to the radiation source. The targetimage data may include an image quality higher than an image quality ofthe image data caused by the scatter data included in the image data.

In some embodiments, the determining, based on the trained machinelearning model and the image data, target image data of the subjectcorresponding to the radiation source may include determining a scatterdistribution associated with the subject by inputting the image datainto the trained machine learning model; and determining, based on thescatter distribution and the image data, the target image data.

In some embodiments, the determining, based on the trained machinelearning model and the image data, target image data of the subjectcorresponding to the radiation source may include determining the targetimage data by inputting the image data into the trained machine learningmodel.

In some embodiments, the trained machine learning may be provided by aprocess including obtaining a plurality of training samples each ofwhich includes image data of a sample subject including scatter data anda reference scatter distribution included in the image data of thesample subject; and training a preliminary machine learning model viaperforming multiple iterations, each iteration including updatingparameter values of the preliminary machine learning model based on adifference between the reference scatter distribution and an estimatedscatter distribution generated by the preliminary machine learning modelbased on the inputted image data.

In some embodiments, the obtaining a plurality of training samples mayinclude, for one of the at least a portion of the plurality of radiationsources, obtaining the image data of the sample subject acquired by theimaging device when the beam stop array is not arranged on a path ofradiation beams emitted by the radiation source. The obtaining aplurality of training samples may include, for one of the at least aportion of the plurality of radiation sources, obtaining first imagedata of the sample subject acquired by the imaging device when the beamstop array is arranged on a path of radiation beams emitted by theradiation source. The obtaining a plurality of training samples mayinclude, for one of the at least a portion of the plurality of radiationsources, determining, based on the first image data, the referencescatter distribution associated with the sample subject included in theimage data of the sample subject.

According to a third aspect of the present disclosure, a system forimaging via an imaging device including a plurality of radiation sourcesand a detector is provided. The system may include at least one storagedevice storing executable instructions, and at least one processor incommunication with the at least one storage device. When executing theexecutable instructions, the at least one processor may cause the systemto perform one or more of the following operations. For each of at leasta portion of the plurality of radiation sources, the operations mayinclude obtaining image data of a subject acquired by the imaging devicevia scanning the subject based on radiation beams emitted by theradiation source. For each of at least a portion of the plurality ofradiation sources, the operations may include obtaining a calibrationmodel corresponding to the radiation source. The calibration model mayindicate a transform relationship between a position of each pixel inthe image data and a position of a portion of the subject represented bythe pixel in a space. For each of at least a portion of the plurality ofradiation sources, the operations may further include determining, basedon the image data of the subject, target image data of the subject usingthe calibration model.

In some embodiments, the determining, based on the image data of thesubject, target image data of the subject using the calibration modelmay include performing a three-dimensional reconstruction operation onthe image data corresponding to at least a portion of the plurality ofradiation sources using multiple calibration models each of whichcorresponds to one of at least a portion of the plurality of radiationsources.

In some embodiments, the calibration model may be provided by a process.The process may include obtaining image data of a reference objectacquired by the imaging device scanning the reference object. Thereference object may include a support and multiple elements arranged onthe support. Each of the multiple elements may include a material withan attenuation coefficient being different from an attenuationcoefficient of a material of the support. The image data may includerepresentations of at least six elements among the multiple elements.The process may include determining a first position of each of the atleast six elements in the image data. The process may also includedetermining a second position of each of the at least six elements in aspace where the imaging device is arranged. The process may furtherinclude determining, based on the first position and the secondposition, the calibration model.

In some embodiments, the determining, based on the first position andthe second position, the calibration model may include determining,based on the first position and the second position, multiple pairs ofpositions each of which includes the first position and the secondposition of a same element among the at least six elements; anddetermining, based on the multiple pairs of positions, the calibrationmodel.

In some embodiments, the first positions of six elements in the at leastsix elements may be different.

In some embodiments, one or more elements among the at least sixelements may not be overlapped on transmission paths of the radiationbeams emitted by the radiation source.

In some embodiments, an interval between two adjacent elements in the atleast six elements may be determined based on at least one of a firstdistance between the radiation source or the reference object and asecond distance between the radiation source and the detector of theimaging device.

According to a fourth aspect of the present disclosure, a system forgeometric calibration for an imaging device including a plurality ofradiation sources and a detector is provided. The system may include atleast one storage device storing executable instructions, and at leastone processor in communication with the at least one storage device.When executing the executable instructions, the at least one processormay cause the system to perform one or more of the following operations.For each of at least a portion of the plurality of radiation sources,the operations may include obtaining image data of a reference objectacquired by the imaging device scanning the reference object. Thereference object may include a support and multiple elements arranged onthe support. Each of the multiple elements may include a material withan attenuation coefficient being different from an attenuationcoefficient of a material of the support. The image data may includerepresentations of at least six elements among the multiple elements.For each of at least a portion of the plurality of radiation sources,the operations may include determining a first position of each of theat least six elements in the image data. For each of at least a portionof the plurality of radiation sources, the operations may also includedetermining a second position of each of the at least six elements in aspace where the imaging device is arranged. For each of at least aportion of the plurality of radiation sources, the operations mayfurther include determining, based on the first position and the secondposition, a calibration model.

In some embodiments, the determining, based on the first position andthe second position, a calibration model may include determining, basedon the first position and the second position, multiple pairs ofpositions each of which includes the first position and the secondposition of a same element among the at least six elements; anddetermining, based on the multiple pairs of positions, the calibrationmodel.

In some embodiments, the first positions of six elements in the at leastsix elements may be different.

In some embodiments, one or more elements among the at least sixelements may not be overlapped on transmission paths of the radiationbeams emitted by the radiation source.

In some embodiments, an interval between two adjacent elements in the atleast six elements may be determined based on at least one of a firstdistance between the radiation source or the reference object and asecond distance between the radiation source and the detector of theimaging device.

Additional features will be set forth in part in the description whichfollows, and in part will become apparent to those skilled in the artupon examination of the following and the accompanying drawings or maybe learned by production or operation of the examples. The features ofthe present disclosure may be realized and attained by practice or useof various aspects of the methodologies, instrumentalities, andcombinations set forth in the detailed examples discussed below.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of exemplaryembodiments. These exemplary embodiments are described in detail withreference to the drawings. The drawings are not scaled. Theseembodiments are non-limiting exemplary embodiments, in which likereference numerals represent similar structures throughout the severalviews of the drawings, and wherein:

FIG. 1 is a schematic diagram illustrating an exemplary imaging systemaccording to some embodiments of the present disclosure;

FIG. 2 is a schematic diagram illustrating an exemplary medical deviceaccording to some embodiments of the present disclosure;

FIG. 3 is a schematic diagram illustrating an exemplary medical deviceaccording to some embodiments of the present disclosure;

FIG. 4 is a schematic diagram illustrating an exemplary beam stop arrayaccording to some embodiments of the present disclosure;

FIG. 5 is a schematic diagram illustrating hardware and/or softwarecomponents of an exemplary computing device on which the processingdevice may be implemented according to some embodiments of the presentdisclosure;

FIG. 6 is a schematic diagram illustrating hardware and/or softwarecomponents of an exemplary mobile device according to some embodimentsof the present disclosure;

FIG. 7 is a block diagram illustrating an exemplary processing deviceaccording to some embodiments of the present disclosure;

FIG. 8 is a schematic flowchart illustrating an exemplary process forimaging according to some embodiments of the present disclosure;

FIG. 9 is a block diagram illustrating an exemplary processing deviceaccording to some embodiments of the present disclosure;

FIG. 10 is a schematic diagram illustrating an exemplary process fordetermining a calibration model according to some embodiments of thepresent disclosure;

FIG. 11 is a schematic diagram illustrating imaging based on a planararray radiation source according to some embodiments of the presentdisclosure;

FIG. 12 is a schematic diagram illustrating an exemplary referenceobject according to some embodiments of the present disclosure;

FIG. 13 is a schematic diagram illustrating an exemplary arrangement ofelements in a reference object according to some embodiments of thepresent disclosure;

FIG. 14 is a schematic diagram illustrating imaging of elements in areference object according to some embodiments of the presentdisclosure;

FIG. 15 is a block diagram illustrating another exemplary processingdevice according to some embodiments of the present disclosure;

FIG. 16 is a schematic diagram illustrating an exemplary process fordetermining target image data of a subject according to some embodimentsof the present disclosure;

FIG. 17 is a block diagram illustrating another exemplary processingdevice according to some embodiments of the present disclosure;

FIG. 18 is a schematic diagram illustrating an exemplary process fordetermining target image data of a subject according to some embodimentsof the present disclosure;

FIG. 19 is a block diagram illustrating an exemplary processing deviceaccording to some embodiments of the present disclosure; and

FIG. 20 is a schematic flowchart illustrating an exemplary trainingprocess of a trained machine learning model according to someembodiments of the present disclosure.

DETAILED DESCRIPTION

The following description is presented to enable any person skilled inthe art to make and use the present disclosure and is provided in thecontext of a particular application and its requirements. Variousmodifications to the disclosed embodiments will be readily apparent tothose skilled in the art, and the general principles defined herein maybe applied to other embodiments and applications without departing fromthe spirit and scope of the present disclosure. Thus, the presentdisclosure is not limited to the embodiments shown but is to be accordedthe widest scope consistent with the claims.

The terminology used herein is for the purpose of describing particularexample embodiments only and is not intended to be limiting. As usedherein, the singular forms “a,” “an,” and “the” may be intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprise,”“comprises,” and/or “comprising,” “include,” “includes,” and/or“including” when used in this disclosure, specify the presence of statedfeatures, integers, steps, operations, elements, and/or components, butdo not preclude the presence or addition of one or more other features,integers, steps, operations, elements, components, and/or groupsthereof.

Generally, the word “module,” “unit,” or “block,” as used herein, refersto logic embodied in hardware or firmware, or to a collection ofsoftware instructions. A module, a unit, or a block described herein maybe implemented as software and/or hardware and may be stored in any typeof non-transitory computer-readable medium or other storage devices. Insome embodiments, a software module/unit/block may be compiled andlinked into an executable program. It will be appreciated that softwaremodules can be callable from other modules/units/blocks or fromthemselves, and/or may be invoked in response to detected events orinterrupts. Software modules/units/blocks configured for execution oncomputing devices may be provided on a computer-readable medium, such asa compact disc, a digital video disc, a flash drive, a magnetic disc, orany other tangible medium, or as a digital download (and can beoriginally stored in a compressed or installable format that needsinstallation, decompression, or decryption prior to execution). Suchsoftware code may be stored, partially or fully, on a storage device ofthe executing computing device, for execution by the computing device.Software instructions may be embedded in firmware, such as an erasableprogrammable read-only memory (EPROM). It will be further appreciatedthat hardware modules/units/blocks may be included in connected logiccomponents, such as gates and flip-flops, and/or can be included ofprogrammable units, such as programmable gate arrays or processors. Themodules/units/blocks or computing device functionality described hereinmay be implemented as software modules/units/blocks but may berepresented in hardware or firmware. In general, themodules/units/blocks described herein refer to logicalmodules/units/blocks that may be combined with othermodules/units/blocks or divided into sub-modules/sub-units/sub-blocksdespite their physical organization or storage. The description may beapplicable to a system, an engine, or a portion thereof.

It will be understood that the term “system,” “engine,” “unit,”“module,” and/or “block” used herein are one method to distinguishdifferent components, elements, parts, sections, or assembly ofdifferent levels in ascending order. However, the terms may be displacedby another expression if they achieve the same purpose.

It will be understood that when a unit, engine, module, or block isreferred to as being “on,” “connected to,” or “coupled to,” anotherunit, engine, module, or block, it may be directly on, connected orcoupled to, or communicate with the other unit, engine, module, orblock, or an intervening unit, engine, module, or block may be present,unless the context clearly indicates otherwise. As used herein, the term“and/or” includes any and all combinations of one or more of theassociated listed items.

These and other features, and characteristics of the present disclosure,as well as the methods of operation and functions of the relatedelements of structure and the combination of parts and economies ofmanufacture, may become more apparent upon consideration of thefollowing description with reference to the accompanying drawings, allof which form a part of this disclosure. It is to be expresslyunderstood, however, that the drawings are for the purpose ofillustration and description only and are not intended to limit thescope of the present disclosure. It is understood that the drawings arenot to scale.

The flowcharts used in the present disclosure illustrate operations thatsystems implement according to some embodiments in the presentdisclosure. It is to be expressly understood, the operations of theflowchart may be implemented not in order. Conversely, the operationsmay be implemented in an inverted order, or simultaneously. Moreover,one or more other operations may be added to the flowcharts. One or moreoperations may be removed from the flowcharts.

Provided herein are systems and methods for imaging via an imagingdevice including a plurality of radiation sources. An aspect of thepresent disclosure relates to systems and methods for imaging using abeam stop array. Each of at least a portion of the plurality ofradiation sources may be configured with a beam stop array that isconfigured to block at least a portion of radiation beams emitted by theradiation source. For one of the at least a portion of the plurality ofradiation sources, the systems and methods may obtain first image dataof a subject acquired by the imaging device when the beam stop array isarranged on a path of radiation beams emitted by the radiation source.The systems and methods may obtain second image data of the subjectacquired by the imaging device when the beam stop array is not arrangedon the path of radiation beams emitted by the radiation source. Thesystems and methods may also determine, based on the first image data, ascatter distribution associated with the subject included in the secondimage data. Further, the systems and methods may determine, based on thescatter distribution and the second image data, third image data of thesubject corresponding to each of the at least a portion of the pluralityof radiation sources. By using the beam stop array, the scatterdistribution associated with the subject may be effectively andaccurately determined and a scatter correction may be performed based onthe scatter distribution, which improves the accuracy and quality ofimaging. In some embodiments, the scatter distribution associated withthe subject may be determined based on a trained machine learning model,which may improve efficiency and accuracy of scatter distributiondetermination, thereby improving the efficiency and accuracy of imagingcorrection.

Another aspect of the present disclosure relates to systems and methodsfor imaging using a reference object. For each of at least a portion ofthe plurality of radiation sources, the systems and methods may obtainimage data of a subject acquired by the imaging device via scanning thesubject based on radiation beams emitted by the radiation source. Thesystems and methods may obtain a calibration model corresponding to theradiation source, the calibration model indicating a transformrelationship between a position of each pixel in the image data and aposition of a portion of the subject represented by the pixel in aspace. The systems and methods may determine target image data of thesubject using the calibration model based on the image data of thesubject. By using the calibration model of each radiation source, thechange of one or more geometric parameters of the imaging device causedby mechanical accuracy errors during the service of the imaging devicemay be calibrated or offset, thereby improving the quality of imaging.

FIG. 1 is a schematic diagram illustrating an exemplary imaging systemaccording to some embodiments of the present disclosure. In someembodiments, the imaging system 100 may be applied to any applicationscenario in which radiation rays (e.g., X-rays) are used for generatingimages and/or providing treatment, such as a computed tomography (CT)system, a digital radiography (DR) system, a C-arm X-ray system, acomputed tomography-positron emission tomography (CT-PET) system, animage-guide radiotherapy (IGRT) system (e.g., a CT guided radiotherapysystem), or the like, or a combination thereof. In some embodiments, theimaging system 100 may include modules and/or components for performingimaging and/or related analysis.

Merely by way of example, as illustrated in FIG. 1 , the imaging system100 may include a medical device 110, a processing device 120, a storagedevice 130, one or more terminals 140, and a network 150. The componentsin the imaging system 100 may be connected in one or more of variousways. Merely by way of example, the medical device 110 may be connectedto the processing device 120 through the network 150. As anotherexample, the medical device 110 may be connected to the processingdevice 120 directly as illustrated in FIG. 1 . As a further example, theterminal(s) 140 may be connected to another component of the imagingsystem 100 (e.g., the processing device 120) via the network 150. Asstill a further example, the terminal(s) 140 may be connected to theprocessing device 120 directly as illustrated by the dotted arrow inFIG. 1 . As still a further example, the storage device 130 may beconnected to another component of the imaging system 100 (e.g., theprocessing device 120) directly as illustrated in FIG. 1 , or throughthe network 150. In some embodiments, one or more components in theimaging system 100 may be omitted. Merely by way of example, the imagingsystem 100 may not include the terminal(s) 140.

The medical device 110 may be configured to acquire imaging datarelating to at least one part of a subject. The imaging data relating toat least one part of a subject may include an image (e.g., an imageslice), projection data, or a combination thereof. In some embodiments,the imaging data may be two-dimensional (2D) imaging data,three-dimensional (3D) imaging data, four-dimensional (4D) imaging data,or the like, or any combination thereof. The subject may be biologicalor non-biological. For example, the subject may include a patient, aman-made subject, etc. As another example, the subject may include aspecific portion, organ, and/or tissue of the patient. For example, thesubject may include the head, the neck, the thorax, the heart, thestomach, a blood vessel, soft tissue, a tumor, nodules, or the like, orany combination thereof.

In some embodiments, the medical device 110 may be a non-invasivebiomedical medical device for disease diagnostic or research purposes.It should be noted that the scanner described above is merely providedfor illustration purposes, and not intended to limit the scope of thepresent disclosure. For illustration purposes, the present disclosuremainly describes systems and methods relating to an X-ray imagingsystem. It should be noted that the X-ray imaging system described belowis merely provided as an example, and not intended to limit the scope ofthe present disclosure. The systems and methods disclosed herein may beapplied to any other imaging systems.

In some embodiments, the medical device 110 may be or include an X-rayimaging device, for example, a computed tomography (CT) scanner, adigital radiography (DR) scanner (e.g., a mobile digital radiography), adigital creast tomosynthesis (DBT) scanner, a digital subtractionangiography (DSA) scanner, a dynamic spatial reconstruction (DSR)scanner, an X-ray microscopy scanner, a multimodality scanner, etc. Forexample, the X-ray imaging device may include a support, one or moreX-ray sources, and a detector. The support may be configured to supportthe X-ray sources and/or the detector. The X-ray sources may beconfigured to emit X-rays toward the target subject to be scanned. Thedetector may be configured to detect X-rays passing through the targetsubject. In some embodiments, the X-ray imaging device may be, forexample, a C-shape X-ray imaging device, an upright X-ray imagingdevice, a suspended X-ray imaging device, or the like. In someembodiments, the medical device 110 may include multiple radiationsources (e.g., X-ray sources) that are arranged as an array. Each of themultiple radiation sources may correspond to a region of the detector.The radiation rays emitted by each of the multiple radiation sources mayreceive by detecting units in the region of the detector correspondingto the radiation source. More descriptions for the medical device 110may be found in elsewhere in the present disclosure (e.g., FIG. 2 andthe descriptions thereof).

The processing device 120 may process data and/or information obtainedfrom the medical device 110, the terminal(s) 140, and/or the storagedevice 130. For example, the processing device 120 may obtain image dataof a subject acquired by an imaging device (e.g., the medical device110) via scanning the subject based on radiation beams emitted by theradiation source. The processing device 120 may also obtain acalibration model corresponding to the radiation source. The calibrationmodel may indicate a transform relationship between a position of eachpixel in the image data and a position of a portion of the subjectrepresented by the pixel in a space. The processing device 120 mayfurther determine target image data using the calibration model based onthe image data. As another example, the processing device 120 may obtainfirst image data of a subject acquired by an imaging device (e.g., themedical device 110) when the beam stop array is arranged on a path ofradiation beams emitted by the radiation source. The processing device120 may also obtain second image data of the subject acquired by theimaging device when the beam stop array is not arranged on the path ofradiation beams emitted by the radiation source. The processing device120 may determine, based on the first image data, a scatter distributionassociated with the subject included in the second image data. Theprocessing device 120 may further determine, based on the scatterdistribution and the second image data, third image data of the subjectcorresponding to each of the at least a portion of the plurality ofradiation sources. As still another example, the processing device 120may obtain image data of a subject acquired by an imaging device (e.g.,the medical device 110) via scanning the subject based on radiationbeams emitted by the radiation source. The image data may includescatter data caused by a scattering of at least a portion of theradiation beams passing through the subject. The processing device 120may also obtain a trained machine learning model. The processing device120 may further determine, based on the trained machine learning modeland the image data, target image data of the subject corresponding tothe radiation source. The target image data may include an image qualityhigher than an image quality of the image data caused by the scatterdata included in the image data.

The trained machine learning model used in the present disclosure (e.g.,the trained machine learning model) may be updated from time to time,e.g., periodically or not, based on a sample set that is at leastpartially different from the original sample set from which the originaltrained machine learning model is determined. For instance, the trainedmachine learning model may be updated based on a sample set includingnew samples that are not in the original sample set. In someembodiments, the determination and/or updating of the trained machinelearning model may be performed on a processing device, while theapplication of the trained machine learning model may be performed on adifferent processing device. In some embodiments, the determinationand/or updating of the trained machine learning model may be performedon a processing device of a system different than the imaging system 100or a server different than a server including the processing device 120on which the application of the trained machine learning model isperformed. For instance, the determination and/or updating of thetrained machine learning model may be performed on a first system of avendor who provides and/or maintains such a machine learning modeland/or has access to training samples used to determine and/or updatethe trained machine learning model, while imaging correction based onthe provided machine learning model may be performed on a second systemof a client of the vendor. In some embodiments, the determination and/orupdating of the trained machine learning model may be performed onlinein response to a request for imaging correction. In some embodiments,the determination and/or updating of the trained machine learning modelmay be performed offline.

In some embodiments, the processing device 120 may be a computer, a userconsole, a single server, or a server group, etc. The server group maybe centralized or distributed. In some embodiments, the processingdevice 120 may be local or remote. For example, the processing device120 may access information and/or data stored in the medical device 110,the terminal(s) 140, and/or the storage device 130 via the network 150.As another example, the processing device 120 may be directly connectedto the medical device 110, the terminal(s) 140, and/or the storagedevice 130 to access stored information and/or data. In someembodiments, the processing device 120 may be implemented on a cloudplatform. Merely by way of example, the cloud platform may include aprivate cloud, a public cloud, a hybrid cloud, a community cloud, adistributed cloud, an inter-cloud, a multi-cloud, or the like, or anycombination thereof.

The storage device 130 may store data, instructions, and/or any otherinformation. In some embodiments, the storage device 130 may store dataobtained from the terminal(s) 140 and/or the processing device 120. Thedata may include image data acquired by the processing device 120,algorithms and/or models for processing the image data, etc. Forexample, the storage device 130 may store image data (e.g., X-rayimages, X-ray projection data, etc.) acquired by the medical device 110.As another example, the storage device 130 may store one or morealgorithms for processing the image data, a trained machine learningmodel for imaging correction, etc. In some embodiments, the storagedevice 130 may store data and/or instructions that the processing device120 may execute or use to perform exemplary methods/systems described inthe present disclosure. In some embodiments, the storage device 130 mayinclude a mass storage, removable storage, a volatile read-and-writememory, a read-only memory (ROM), or the like, or any combinationthereof. Exemplary mass storage may include a magnetic disk, an opticaldisk, a solid-state drive, etc. Exemplary removable storage may includea flash drive, a floppy disk, an optical disk, a memory card, a zipdisk, a magnetic tape, etc. Exemplary volatile read-and-write memoriesmay include a random access memory (RAM). Exemplary RAM may include adynamic RAM (DRAM), a double date rate synchronous dynamic RAM (DDRSDRAM), a static RAM (SRAM), a thyristor RAM (T-RAM), and azero-capacitor RAM (Z-RAM), etc. Exemplary ROM may include a mask ROM(MROM), a programmable ROM (PROM), an erasable programmable ROM (EPROM),an electrically erasable programmable ROM (EEPROM), a compact disk ROM(CD-ROM), and a digital versatile disk ROM, etc. In some embodiments,the storage device 130 may be implemented on a cloud platform. Merely byway of example, the cloud platform may include a private cloud, a publiccloud, a hybrid cloud, a community cloud, a distributed cloud, aninter-cloud, a multi-cloud, or the like, or any combination thereof.

In some embodiments, the storage device 130 may be connected to thenetwork 150 to communicate with one or more other components in theimaging system 100 (e.g., the processing device 120, the terminal(s)140, etc.). One or more components in the imaging system 100 may accessthe data or instructions stored in the storage device 130 via thenetwork 150. In some embodiments, the storage device 130 may be directlyconnected to or communicate with one or more other components in theimaging system 100 (e.g., the processing device 120, the terminal(s)140, etc.). In some embodiments, the storage device 130 may be part ofthe processing device 120.

The terminal(s) 140 may include a mobile device 141, a tablet computer140-2, a laptop computer 143, or the like, or any combination thereof.In some embodiments, the mobile device 141 may include a smart homedevice, a wearable device, a mobile device, a virtual reality device, anaugmented reality device, or the like, or any combination thereof. Insome embodiments, the smart home device may include a smart lightingdevice, a control device of an intelligent electrical apparatus, a smartmonitoring device, a smart television, a smart video camera, aninterphone, or the like, or any combination thereof. In someembodiments, the wearable device may include a bracelet, a footgear,eyeglasses, a helmet, a watch, clothing, a backpack, a smart accessory,or the like, or any combination thereof. In some embodiments, the mobiledevice may include a mobile phone, a personal digital assistant (PDA), agaming device, a navigation device, a point of sale (POS) device, alaptop, a tablet computer, a desktop, or the like, or any combinationthereof. In some embodiments, the virtual reality device and/or theaugmented reality device may include a virtual reality helmet, virtualreality glasses, a virtual reality patch, an augmented reality helmet,augmented reality glasses, an augmented reality patch, or the like, orany combination thereof. For example, the virtual reality device and/orthe augmented reality device may include a Google Glass™, an OculusRift™, a Hololens™, a Gear VR™, etc. In some embodiments, theterminal(s) 140 may be part of the processing device 120.

The network 150 may include any suitable network that can facilitate theexchange of information and/or data for the imaging system 100. In someembodiments, one or more components of the medical device 110 (e.g., anMRI device, a PET device, etc.), the terminal(s) 140, the processingdevice 120, the storage device 130, etc., may communicate informationand/or data with one or more other components of the imaging system 100via the network 150. For example, the processing device 120 may obtaindata from the medical device 110 via the network 150. As anotherexample, the processing device 120 may obtain user instructions from theterminal(s) 140 via the network 150. The network 150 may be and/orinclude a public network (e.g., the Internet), a private network (e.g.,a local area network (LAN), a wide area network (WAN)), etc.), a wirednetwork (e.g., an Ethernet network), a wireless network (e.g., an 802.11network, a Wi-Fi network, etc.), a cellular network (e.g., a Long TermEvolution (LTE) network), a frame relay network, a virtual privatenetwork (“VPN”), a satellite network, a telephone network, routers,hubs, switches, server computers, and/or any combination thereof. Merelyby way of example, the network 150 may include a cable network, awireline network, a fiber-optic network, a telecommunications network,an intranet, a wireless local area network (WLAN), a metropolitan areanetwork (MAN), a public telephone switched network (PSTN), a Bluetooth™network, a ZigBee™ network, a near field communication (NFC) network, orthe like, or any combination thereof. In some embodiments, the network150 may include one or more network access points. For example, thenetwork 150 may include wired and/or wireless network access points suchas base stations and/or internet exchange points through which one ormore components of the imaging system 100 may be connected to thenetwork 150 to exchange data and/or information.

It should be noted that the above description of the imaging system 100is merely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations and modifications may be madeunder the teachings of the present disclosure. For example, the assemblyand/or function of the imaging system 100 may be varied or changedaccording to specific implementation scenarios.

FIG. 2 is a schematic diagram illustrating an exemplary medical device200 according to some embodiments of the present disclosure. The medicaldevice 200 may be an exemplary embodiment of the medical device 110 asdescribed in connection with FIG. 1 . In some embodiments, the medicaldevice 200 may be an X-ray imaging device. The medical device 200 mayinclude a gantry 210, a supporting component 220, and an imagingcomponent 230.

The gantry 210 may be configured to provide a support for the supportingcomponent 220 and the imaging component 230.

The supporting component 220 may be configured to provide a support forthe imaging component 230. In some embodiments, the supporting component220 may be moveably connected with the gantry 210. In some embodiments,the supporting component 220 may include a driving component. Thedriving component may be configured to drive the supporting component220 to move by, e.g., translating and/or rotating. For example, thesupporting component 220 may be driven to move a certain height in avertical direction along the gantry 210 by the driving componentaccording to a target height of a subject. As another example, thesupporting component 220 may be driven to rotate around a connectionbetween the supporting component 220 and the gantry 210 to form acertain angle according to a target angle of a subject. The imagingcomponent 230 may be installed on the supporting component 220. Theimaging component 230 may move along with the movement of the supportingcomponent 220. For example, when the supporting component 220 moves inthe vertical direction along the gantry 210, the imaging component 230may move in the vertical direction along with the movement of thesupporting component 220. When the supporting component 220 rotates to acertain angle, the imaging component 230 may rotate to the certain anglealong with the rotation of the supporting component 220.

The imaging component 230 may include a radiation beam generationcomponent 231 and a detector 232. The radiation beam generationcomponent 231 may generate and emit radiation beams to the subject. Theradiation beams may include a particle ray, a photon ray, or the like,or a combination thereof. In some embodiments, the radiation beams mayinclude a plurality of radiation particles (e.g., neutrons, protons,electrons, p-mesons, heavy ions), a plurality of radiation photons(e.g., X-rays, y-rays, ultraviolet, laser), or the like, or acombination thereof. In some embodiments, the radiation beam generationcomponent 231 may include a plurality of light sources (also referred toas radiation sources) arranged as an array. In some embodiments, theplurality of radiation sources may be arranged in a planar plane. Insome embodiments, the plurality of radiation sources may be arranged ina curved plane. In some embodiments, the plurality of radiation sourcesmay be arranged in different planar planes. More descriptions regardingthe radiation beam generation component 231 may be found in elsewhere inthe present disclosure (e.g., FIGS. 3 and 4 and the descriptionsthereof).

The detector 232 may detect radiation beams emitted from the radiationbeam generation component 231. In some embodiments, the detector 232 mayinclude a plurality of detecting units. Each of the detecting units mayinclude a crystal element (e.g., a scintillator crystal) and aphotosensor. A crystal element (e.g., a scintillator crystal) mayscintillate when a radiation ray (e.g., y ray) photon impinges on thecrystal element. The crystal element may absorb the energy of theradiation ray (e.g., X ray) photon, and convert the absorbed energy intolight. The crystal element may use one or more types of crystalsincluding, for example, NaI(TI), BGO, LSO, YSO, GSO, LYSO, LaBr₃, LFS,LuAP, LuI₃, BaF₂, CeF, CsI(TI), CsI(Na), CaF₂(Eu), CdWO₄, YAP, or thelike, or any combination thereof. A photosensor may convert a lightsignal (e.g., the light output from a scintillator) to an electricalsignal. The electrical signal may be processed by an electronic circuitto form the projection data. In some embodiments, a photosensor may be aphotomultiplier tube (PMT), a silicon photomultiplier (SiPM), etc.

The detector 232 may include a scintillation detector (e.g., a cesiumiodide detector) or a gas detector. The detector 232 may be a single-rowdetector or a multi-rows detector. In some embodiments, the detector 232may include a flat panel detector. In some embodiments, the detector 232may be movably connected with the supporting component 220. The detector232 may move relative to the supporting component 220. In someembodiments, the radiation beam generation component 231 and thedetector 232 may be respectively disposed at two ends of the supportingcomponent 220. A detection region may be formed between the radiationbeam generation component 231 and the detector 232. A subject 240 (e.g.,a reference object as described elsewhere in the present disclosure) maybe located at the detection region for the acquisition of image data ofthe subject. In some embodiments, a reference object may be arranged inthe detection region to obtain a calibration model corresponding to theradiation source. The reference object may include a phantom. In someembodiments, the reference object may include a support (also referredto as a first support) and multiple elements (also referred to as firstelements) arranged on the support. Each of the multiple elements mayinclude a material with an attenuation coefficient that is differentfrom an attenuation coefficient of a material of the support. Each ofthe plurality of radiation sources in the radiation beam generationcomponent 231 may correspond to a portion of the multiple elements. Asused herein, a radiation source corresponding to a portion of themultiple elements may refer to that radiation beams emitted by theradiation source are able to pass through the portion of the multipleelements. The radiation source corresponding to a portion of themultiple elements may also be referred to the radiation source coveringthe portion of the multiple elements. More descriptions regarding thereference object may be found in elsewhere in the present disclosure(e.g., FIG. 12 and the descriptions thereof).

It should be noted that the examples illustrated in FIG. 2 are providedfor the purposes of illustration, and not intended to limit the scope ofthe present disclosure. For persons having ordinary skills in the art,various modifications and changes in the forms and details of theapplication of the above method and system may occur without departingfrom the principles of the present disclosure. In some embodiments, thegantry 210 may be configured in any suitable manner, such as a C-shapedsupport, a U-shape support, a G-shape support, or the like. In someembodiments, the medical device 200 may include one or more additionalcomponents not described and/or without one or more componentsillustrated in FIG. 2 . For example, the medical device 200 may furtherinclude a camera. In some embodiments, the support component 220 and thegantry 210 may be integrated into one single component.

FIG. 3 is a schematic diagram illustrating an exemplary medical device300 according to some embodiments of the present disclosure. The medicaldevice 300 may be an exemplary embodiment of the medical device 110 asdescribed in connection with FIG. 1 .

In some embodiments, the medical device 300 may include an X-ray imagingdevice including a plurality of radiation sources. The medical device300 may include a detector 310, a radiation beam generation component330, a control device 340, and a blocker 350.

The detector 310 may be configured to detect at least part of radiationbeams (e.g., X-ray photons) emitted by the radiation beam generationcomponent 330. In some embodiments, the detector 310 may be arrangedopposite to the radiation beam generation component 330. In someembodiments, the detector 310 may include multiple detecting unitsarranged in a plane substantially perpendicular to the central axis of aradiation beam (e.g., X-rays) emitted by the radiation beam generationcomponent 330 (shown as dotted line as shown in FIG. 3 ). The radiationbeam (e.g., X-rays) emitted by the radiation beam generation component330 may be received or detected by the detector 310. In someembodiments, the subject 320 may include a biological body or anon-biological body. Merely by way of example, the subject 320 mayinclude a breast. More descriptions regarding the detector may be foundin elsewhere in the present disclosure (e.g., FIG. 2 and thedescriptions thereof).

In some embodiments, the radiation beam generation component 330 mayinclude a plurality of radiation sources (also referred to as lightsources) and at least one radiation source panel. A radiation source inthe radiation beam generation component 330 may include a field emissioncold cathode ray source. Each of one or more radiation sources in theradiation beam generation component 330 may emit the radiation beams(e.g., X-rays) to the subject 320. In some embodiments, at least two ofthe plurality of radiation sources may operate independently. In someembodiments, at least two of the plurality of radiation sources mayoperate synchronously.

In some embodiments, the plurality of radiation sources may be arrangedat the radiation source panel in a predetermined arrangement. Forexample, the plurality of radiation sources may be arranged at a certaininterval (e.g., a same interval, a proportional interval, etc.). Asanother example, the plurality of radiation sources may be arranged toform a certain shape (e.g., a circle, a square, a rhombus, a trapezium,an arc, etc.). In some embodiments, the radiation beam generationcomponent 330 may include two or more radiation source panels arrangedat a certain angle. In some embodiments, the angle between the radiationsource panels may be adjusted. For example, the radiation source panelsmay be rotated relative to each other to adjust the angle between theradiation source panels. In some embodiments, the angle range betweenthe radiation source panels may be in a range from 140 to 180 degrees.In some embodiments, the angle range between the radiation source panelsmay be in a range from 90 to 180 degrees. In some embodiments, the anglerange between the radiation source panels may be in a range from 60 to180 degrees. In some embodiments, the angle range between the radiationsource panels may be in a range from 30 to 180 degrees. In someembodiments, the angle range between the radiation source panels may bein a range from 0 to 180 degrees.

In some embodiments, the radiation beam generation component 330 may bemounted on a gantry. For example, the radiation beam generationcomponent 330 may be mounted on a supporting component that is movablyconnected with the gantry. The plurality of radiation sourcesdistributed in different positions of the radiation source panels mayachieve different radiation angles without moving the gantry. In someembodiments, at least one of the plurality of radiation sources may bephysically connected with the radiation source panel in a non-detachablemanner. In some embodiments, at least one of the plurality of radiationsources may be detachably connected to the radiation source panel tofacilitate maintenance and replacement of each radiation source.

The control device 340 may include a processing device for controllingcomponents of the medical device 300 to perform an imaging operation. Insome embodiments, the control device 340 may control radiationparameters of each radiation source in the radiation beam generationcomponent 330 to obtain the image data of the subject 320 under theradiation parameters. In some embodiments, the radiation parameter of aradiation source in the radiation beam generation component 330 mayinclude a position of the radiation source in the radiation beamgeneration component 330, a corresponding radiation dose, etc. In someembodiments, the control device 340 may obtain a control instructioninput manually. The control instruction may be configured to instructthe control device 340 to control the radiation parameter of eachradiation source in the radiation beam generation component 330. Forexample, the control device 340 may obtain a control instructionmanually input by a user through a terminal (e.g., the terminal 140). Insome embodiments, the control device 340 may automatically control theradiation parameter of each radiation source in the radiation beamgeneration component 330. For example, the control device 340 mayautomatically adjust the radiation parameter of each radiation source inthe radiation beam generation component 330 according to information ofthe subject. In some embodiments, the information of the subject mayinclude a height, a weight, an age, historical inspection data, healthindex, etc., or any combination thereof, of the subject. In someembodiments, the control device 340 may select corresponding controlparameters according to a predetermined protocol. For example, a certainscanning protocol, a turn-on sequence, a scanning duration, etc., maycorrespond to a specific radiation dose of the radiation source.

In some embodiments, the blocker 350 may be provided between theradiation beam generation component 330 and the detector 310 in animaging process. For example, the blocker 350 may be located between theradiation beam generation component 330 and the subject 320 in theacquisition of image data of the subject 320. The blocker 350 mayinclude one or more beam stop arrays. Each of the one or more beam stoparrays may correspond to one of the plurality of radiation sources inthe radiation beam generation component 330. Each of the one or morebeam stop arrays may include a support (also referred to as a secondsupport) (e.g., the support 410) and multiple elements (also referred toas second elements) (e.g., the element 420). In some embodiments, eachof the multiple elements may include a material with an attenuationcoefficient exceeding an attenuation coefficient of a material of thesupport. For example, the support may be composed of low-attenuationmaterials, such as, plastic, rubber, aluminum, plexiglass, or the like.The element may be composed of high-attenuation materials, such as,lead, concrete, or the like. The element may be configured to blockradiation beams emitted by a radiation source in the radiation beamgeneration component 330. In some embodiments, the support may have aregular structure, such as, a rectangular parallelepiped, a cylinder, abevel, or the like. In some embodiments, the support may include a plateincluding an irregular structure, such as, a “V”-shaped structure, awave-shaped structure, a folded plate structure, or the like. In someembodiments, the element may be arranged in the support at a sameinterval. In some embodiments, the multiple elements in the beam stoparray may be arranged in the support at different intervals.

Referring to FIG. 4 , FIG. 4 is a schematic diagram illustrating anexemplary beam stop array according to some embodiments of the presentdisclosure. As shown in FIG. 4 , multiple elements 420 of the beam stoparray may be arranged on a support 410 in a form of a rectangular array,for example, a 7×8 rectangular array. The white part may be the support410, and the black part may be the elements 420.

In some embodiments, a count (or number) of beam stop arrays in theblocker 350 may be the same as a count of radiation sources in theradiation beam generation component 330. Each beam stop array maycorrespond to one radiation source in the radiation beam generationcomponent 330. For example, if the radiation beam generation component330 includes 10 radiation sources, the blocker 350 may include 10 beamstop arrays. In some embodiments, the count of beam stop arrays in theblocker 350 may be less than the count of radiation sources in theradiation beam generation component 330. One of the beam stop arrays inthe blocker may correspond to one single radiation source in theradiation beam generation component 330. Alternatively, one radiationsource in the radiation beam generation component 330 may not have acorresponding beam stop array in the blocker 350. For example, c mayinclude 10 radiation sources, and the count of the beam stop array 350may be 1, or 2, or 3, . . . , or 9 (any count less than 10), etc. Whenthe count of beam stop arrays in the blocker 350 is less than the countof radiation sources in the radiation beam generation component 330,each of one or more radiation sources in the radiation beam generationcomponent 330 may be not configured with a beam stop array.

The one or more beam stop arrays in the blocker 350 may be independent.In some embodiments, the one or more beam stop arrays in the blocker 350may be detachably connected with the medical device 300. When a beamstop array of the blocker 350 is needed to be arranged on a path of aradiation beam emitted by a radiation source in the radiation beamgeneration component 330, the beam stop array of the blocker 350 may bemounted on the medical device 300 between the radiation beam generationcomponent 330 and the subject 220; when the beam stop array of theblocker 350 is not needed to be arranged on the path of the radiationbeam emitted by the radiation source in the radiation beam generationcomponent 330, the beam stop array of the blocker 350 may be removedfrom the medical device 300.

In some embodiments, the one or more beam stop arrays in the blocker 350may be movably. For example, a beam stop array in the blocker 350 maymove to a position between the radiation beam generation component 330and the subject 320. For example, the beam stop array in the blocker 350may be moved to and arranged on a path of radiation beams emitted by theradiation beam generation component 330 using a transmission device. Asanother example, the beam stop array in the blocker 350 may move awayfrom a position between the radiation beam generation component 330 andthe subject 320. For example, the beam stop array in the blocker 350 maybe moved away from and not arranged on the path of radiation beamsemitted by the radiation beam generation component 330 using thetransmission device. In some embodiments, a beam stop array in theblocker 350 may be controlled automatically or manually by the controldevice 340 to block part or all paths of the radiation sources in theradiation beam generation component 330 or to remove from some or allpaths of the radiation sources in the radiation beam generationcomponent 330 by sliding, rotating, etc.

In some embodiments, the detector 310 may be fixedly arranged relativeto the gantry, and the radiation beam generation component 330 and theblocker 350 may be movably arranged relative to the gantry. As shown inFIG. 3 , the radiation beam generation component 330 and the blocker 350may be arranged on a moving rail of the gantry. Therefore, the radiationbeam generation component 330 and the blocker 350 may be driven to moveup and down along the moving rail of the gantry to adjust a distancebetween the radiation beam generation component 330 and the subject 320and a distance between the blocker 350 and the subject 320.Correspondingly, a distance between the radiation source 330 and thedetector 310 (i.e., a source to image receptor distance (SID)) may beadjusted by controlling the radiation beam generation component 330 tomove along the moving rail of the gantry. Alternatively, a distancebetween the blocker 350 and the detector 310 may be adjusted bycontrolling the blocker 350 to move along the gantry. In someembodiments, the detector 310 and the blocker 350 may be movablyarranged relative to the gantry, and the radiation beam generationcomponent 330 may be fixedly arranged relative to the gantry. Forexample, the detector 310 and the blocker 350 may be arranged on themoving rail of the gantry. Therefore, the detector 310 and the beam stoparray 350 may be driven to move up and down along the moving rail of thegantry. The SID may be adjusted by controlling the detector 310 to movealong the moving rail of the gantry. The distance between the blocker350 and the detector 310 may also be adjusted by controlling the blocker350 to move along the moving rail of the gantry. In some embodiments,the SID and the distance between the beam stop array 350 and thedetector may be adjusted automatically or manually by the control device340.

It should be noted that the examples illustrated in FIG. 3 are providedfor the purposes of illustration, and not intended to limit the scope ofthe present disclosure. For persons having ordinary skills in the art,various modifications and changes in the forms and details of theapplication of the above method and system may occur without departingfrom the principles of the present disclosure. In some embodiments, themedical device 300 may further include one or more components. Forexample, the medical device 300 may further include a stage for fixingthe subject 320. The detector 310 may be integrated with the stage orseparated from the stage.

FIG. 5 is a schematic diagram illustrating hardware and/or softwarecomponents of an exemplary computing device 500 on which the processingdevice 120 may be implemented according to some embodiments of thepresent disclosure. As illustrated in FIG. 5 , the computing device 500may include a processor 510, a storage 520, an input/output (I/O) 530,and a communication port 540.

The processor 510 may execute computer instructions (program codes) andperform functions of the processing device 120 in accordance withtechniques described herein. The computer instructions may include, forexample, routines, programs, objects, components, signals, datastructures, procedures, modules, and functions, which perform particularfunctions described herein. For example, the processor 510 may processdata obtained from the medical device 110, the terminal(s) 140, thestorage device 130, and/or any other component of the imaging system100. Specifically, the processor 510 may process image data obtainedfrom the medical device 110. For example, the processor 510 maydetermine target image data using a calibration model based on imagedata. As another example, the processing device 120 may determine, basedon first image data, a scatter distribution associated with the subjectincluded in second image data. In some embodiments, the target imagedata may be stored in the storage device 130, the storage 520, etc. Insome embodiments, the target image data may be displayed on a displaydevice by the I/O 530. In some embodiments, the processor 510 mayperform instructions obtained from the terminal(s) 140. In someembodiments, the processor 510 may include one or more hardwareprocessors, such as a microcontroller, a microprocessor, a reducedinstruction set computer (RISC), an application-specific integratedcircuits (ASICs), an application-specific instruction-set processor(ASIP), a central processing unit (CPU), a graphics processing unit(GPU), a physics processing unit (PPU), a microcontroller unit, adigital signal processor (DSP), a field-programmable gate array (FPGA),an advanced RISC machine (ARM), a programmable logic device (PLD), anycircuit or processor capable of executing one or more functions, or thelike, or any combinations thereof.

Merely for illustration, only one processor is described in thecomputing device 500. However, it should be noted that the computingdevice 500 in the present disclosure may also include multipleprocessors. Thus operations and/or method steps that are performed byone processor as described in the present disclosure may also be jointlyor separately performed by the multiple processors. For example, if inthe present disclosure the processor of the computing device 500executes both operation A and operation B, it should be understood thatoperation A and operation B may also be performed by two or moredifferent processors jointly or separately in the computing device 500(e.g., a first processor executes operation A and a second processorexecutes operation B, or the first and second processors jointly executeoperations A and B).

The storage 520 may store data/information obtained from the medicaldevice 110, the terminal(s) 140, the storage device 130, or any othercomponent of the imaging system 100. In some embodiments, the storage520 may include a mass storage device, a removable storage device, avolatile read-and-write memory, a read-only memory (ROM), or the like,or any combination thereof. For example, the mass storage may include amagnetic disk, an optical disk, a solid-state drive, etc. The removablestorage may include a flash drive, a floppy disk, an optical disk, amemory card, a zip disk, a magnetic tape, etc. The volatileread-and-write memory may include a random access memory (RAM). The RAMmay include a dynamic RAM (DRAM), a double date rate synchronous dynamicRAM (DDR SDRAM), a static RAM (SRAM), a thyristor RAM (T-RAM), and azero-capacitor RAM (Z-RAM), etc. The ROM may include a mask ROM (MROM),a programmable ROM (PROM), an erasable programmable ROM (PEROM), anelectrically erasable programmable ROM (EEPROM), a compact disk ROM(CD-ROM), and a digital versatile disk ROM, etc. In some embodiments,the storage 520 may store one or more programs and/or instructions toperform exemplary methods described in the present disclosure. Forexample, the storage 520 may store a program for the processing device120 for generating attenuation correction data for a PET image.

The I/O 530 may input or output signals, data, and/or information. Insome embodiments, the I/O 530 may enable user interaction with theprocessing device 120. In some embodiments, the I/O 530 may include aninput device and an output device. Exemplary input devices may include akeyboard, a mouse, a touch screen, a microphone, or the like, or acombination thereof. Exemplary output devices may include a displaydevice, a loudspeaker, a printer, a projector, or the like, or acombination thereof. Exemplary display devices may include a liquidcrystal display (LCD), a light-emitting diode (LED)-based display, aflat panel display, a curved screen, a television device, a cathode raytube (CRT), or the like, or a combination thereof.

The communication port 540 may be connected with a network (e.g., thenetwork 150) to facilitate data communications. The communication port540 may establish connections between the processing device 120 and themedical device 110, the terminal(s) 140, or the storage device 130. Theconnection may be a wired connection, a wireless connection, or acombination of both that enables data transmission and reception. Thewired connection may include an electrical cable, an optical cable, atelephone wire, or the like, or any combination thereof. The wirelessconnection may include a Bluetooth network, a Wi-Fi network, a WiMaxnetwork, a WLAN, a ZigBee network, a mobile network (e.g., 3G, 4G, 5G,etc.), or the like, or any combination thereof. In some embodiments, thecommunication port 540 may be a standardized communication port, such asRS232, RS485, etc. In some embodiments, the communication port 540 maybe a specially designed communication port. For example, thecommunication port 540 may be designed in accordance with the digitalimaging and communications in medicine (DICOM) protocol.

FIG. 6 is a schematic diagram illustrating hardware and/or softwarecomponents of an exemplary mobile device 600 according to someembodiments of the present disclosure. As illustrated in FIG. 6 , themobile device 600 may include a communication platform 610, a display620, a graphics processing unit (GPU) 630, a central processing unit(CPU) 640, an I/O 650, a memory 660, and a storage 690. In someembodiments, any other suitable component, including but not limited toa system bus or a controller (not shown), may also be included in themobile device 600. In some embodiments, a mobile operating system 670(e.g., iOS, Android, Windows Phone, etc.) and one or more applications680 may be loaded into the memory 660 from the storage 690 in order tobe executed by the CPU 640. The applications 680 may include a browseror any other suitable mobile apps for receiving and renderinginformation relating to image processing or other information from theprocessing device 120. User interactions with the information stream maybe achieved via the I/O 650 and provided to the processing device 120and/or other components of the imaging system 100 via the network 150.

To implement various modules, units, and functionalities described inthe present disclosure, computer hardware platforms may be used as thehardware platform(s) for one or more of the elements described herein.The hardware elements, operating systems, and programming languages ofsuch computers are conventional in nature, and it is presumed that thoseskilled in the art are adequately familiar therewith to adapt thosetechnologies to generate an image as described herein. A computer withuser interface elements may be used to implement a personal computer(PC) or another type of work station or terminal device, although acomputer may also act as a server if appropriately programmed. It isbelieved that those skilled in the art are familiar with the structure,programming, and general operation of such computer equipment and as aresult, the drawings should be self-explanatory.

FIG. 7 is a block diagram illustrating an exemplary processing deviceaccording to some embodiments of the present disclosure. In someembodiments, processing device 120 may be implemented on a computingdevice 500 (e.g., the processor 510) illustrated in FIG. 5 or a CPU 640as illustrated in FIG. 6 . As illustrated in FIG. 7 , the processingdevice 120 may include an acquisition module 710, a model determinationmodule 720, and a reconstruction module 730. Each of the modulesdescribed above may be a hardware circuit that is designed to performcertain actions, e.g., according to a set of instructions stored in oneor more storage media, and/or any combination of the hardware circuitand the one or more storage media.

The acquisition module 710 may be configured to data related togeometric calibration. In some embodiments, the acquisition module 710may be configured to obtain image data of a subject acquired by animaging device via scanning the subject based on radiation beams emittedby each of at least a portion of a plurality of radiation sources. Insome embodiments, the acquisition module 710 may be configured to obtaina calibration model corresponding to each of at least a portion of aplurality of radiation sources.

The model determination module 720 may be configured to obtain acalibration model corresponding to each of at least a portion of aplurality of radiation sources. In some embodiments, multiplecalibration models may be determined and stored in the storage device bya processing device that is the same as or different from the processingdevice 120. The model determination module 720 may determine one of themultiple calibration models corresponding to the radiation source andobtain the determined calibration model from the storage device. Inssome embodiments, the calibration models stored in the storage devicemay be updated from time to time, e.g., periodically or not according toprocess 1000 as illustrated in FIG. 10 . For example, the calibrationmodels stored in the storage device may be updated per week, per month,etc. In some embodiments, the model determination module 720 maydetermine the calibration model before the acquisition of the image dataaccording to process 1000 as illustrated in FIG. 10 .

The reconstruction module 730 may determine target image data of thesubject based on the image data of the subject using the calibrationmodel.

In some embodiments, the reconstruction module 730 may reconstruct thetarget image data (i.e., a target image) based on the image data (e.g.,the projection data) and the multiple calibration models by performing athree-dimensional (3D) image reconstruction operation on the image dataof the subject.

In some embodiments, the reconstruction module 730 may perform a 2Dreconstruction operation on the projection data corresponding to each ofat least a portion of the plurality of radiation sources to obtain oneor more 2D images. The 2D reconstruction operation may be performed bythe reconstruction module 730 using an iterative algorithm, ananalytical algorithm, etc. The reconstruction module 730 may perform a3D image reconstruction operation on the one or more 2D imagescorresponding to each of at least a portion of the plurality ofradiation sources based on the calibration models.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. Apparently, for persons having ordinary skills inthe art, multiple variations and modifications may be conducted underthe teachings of the present disclosure. However, those variations andmodifications do not depart from the scope of the present disclosure.For example, the model determination module 720 may be integrated intothe acquisition module 710 or omitted from the processing device 120.

FIG. 8 is a schematic flowchart illustrating an exemplary process fordetermining target image data according to some embodiments of thepresent disclosure. In some embodiments, process 800 may be implementedas a set of instructions (e.g., an application) stored in the storagedevice 130, storage 520, or storage 690. The processing device 120, theprocessor 510, and/or the CPU 640 may execute the set of instructions,and when executing the instructions, the processing device 120, theprocessor 510, and/or the CPU 640 may be configured to perform theprocess 800. The operations of the illustrated process presented beloware intended to be illustrative. In some embodiments, the process 800may be accomplished with one or more additional operations not describedand/or without one or more of the operations discussed. Additionally,the order of the operations of the process 800 illustrated in FIG. 8 anddescribed below is not intended to be limiting.

In 810, for each of at least a portion of a plurality of radiationsources, the processing device 120 (e.g., the acquisition module 710)may obtain image data of a subject acquired by an imaging device viascanning the subject based on radiation beams emitted by the radiationsource.

The subject may be biological or non-biological. For example, thesubject may include a patient, a man-made object, etc. As anotherexample, the subject may include a specific portion, organ, and/ortissue of the patient. As still another example, the subject may includea breast.

In some embodiments, the imaging device (e.g., the medical device 110,the medical device 200, the medical device 300) may include theplurality of radiation sources and a detector. In some embodiments, theplurality of radiation sources (also referred to as light sources) maybe arranged as an array to form a planar array radiation source. Thedetector may include a plurality of detecting units each of which isconfigured to receive radiation beams emitted from at least a portion ofthe plurality of radiation sources. Each of the plurality of radiationsources may correspond to at least a portion of the detecting units. Theimage data of the subject may be acquired by detecting unitscorresponding to each of the at least a portion of the plurality ofradiation sources. In some embodiments, the imaging device may include adigital breast tomosynthesis (DBT) device. More descriptions regardingthe imaging device may be found in FIGS. 1-3 and the descriptionsthereof.

In some embodiments, the image data of the subject may includeprojection data acquired by the detector of the imaging device. The atleast a portion of a plurality of radiation sources may operateseparately to emit radiation beams toward the subject for theacquisition of the image data.

In some embodiments, the image data may be obtained from the imagingdevice (e.g., the medical device 110, the medical device 200, themedical device 300). For example, the medical device 110 may acquire theimage data of the subject via scanning the subject based on theradiation beams emitted by the radiation source and transmit theacquired image data of the subject to the processing device 120. In someembodiments, the processing device 120 (e.g., the acquisition module710) may obtain the image data of the subject from a storage device, forexample, the storage device 130, or any other storage. For example, themedical device 110 may acquire the image data of the object via scanningthe subject based on the radiation beams emitted by the radiation sourceand store the acquired image data of the subject in the storage device.The processing device 120 may obtain the image data of the subject fromthe storage device.

In 820, for each of at least a portion of the plurality of radiationsources, the processing device 120 (e.g., the acquisition module 710 orthe model determination module 720) may obtain a calibration modelcorresponding to the radiation source.

The calibration model may indicate a transform relationship between aposition of each pixel in an image of the subject and a position of aportion of the subject represented by the pixel in a space. In someembodiments, the calibration model may be denoted as a matrix, afunction, etc. The calibration model may also be referred to as acalibration matrix, a transformation matrix, a projection matrix, or thelike. In some embodiments, for different radiation sources, thecalibration model may be different. In some embodiments, for differentradiation sources, the calibration model may be the same. Thecalibration model may be obtained based on a reference object (e.g., thereference object 240 or the reference object as illustrated in FIG. 10). More descriptions regarding the determination of the calibrationmodel may be found in FIG. 10 and the descriptions thereof.

In some embodiments, the calibration model may be obtained from astorage device, for example, the storage device 130, or any otherstorage. For example, multiple calibration models may be determined andstored in the storage device by a processing device that is the same asor different from the processing device 120. The processing device 120may determine one of the multiple calibration models corresponding tothe radiation source and obtain the determined calibration model fromthe storage device. Ins some embodiments, the calibration models storedin the storage device may be updated from time to time, e.g.,periodically or not according to process 1000 as illustrated in FIG. 10. For example, the calibration models stored in the storage device maybe updated per week, per month, etc. In some embodiments, the processingdevice 120 may obtain the calibration model before the acquisition ofthe image data according to process 1000 as illustrated in FIG. 10 .

In 830, the processing device 120 (e.g., the reconstruction module 730)may determine target image data of the subject based on the image dataof the subject using the calibration model.

In some embodiments, the processing device 120 may obtain the image datacorresponding to at least a portion of the plurality of radiationsources and multiple calibration models each of which corresponds to theeach of at least a portion of a plurality of radiation sources. Theprocessing device 120 may reconstruct the target image data (i.e., atarget image) based on the image data (e.g., the projection data) andthe multiple calibration models. In some embodiments, the processingdevice 120 (e.g., the reconstruction module 730) may perform athree-dimensional (3D) image reconstruction operation on the image dataof the subject to obtain the target image data (e.g., a 3D target image)of the subject based on the calibration models. The 3D imagereconstruction operation may be formed by the processing device using astepwise approximation algorithm, a forward-backward projectionalgorithm, a Fourier transform algorithm, or the like, or anycombination thereof. For example, using the forward-backward projectionalgorithm, the processing device 120 may perform forward projection andbackward projection based on the calibration model to reconstruct thetarget image data.

Since the calibration model indicates the transform relationship betweena position of each pixel in the image data and a position of a portionof the subject represented by the pixel in the space, the processingdevice 120 may determine the target image data of the subject based onthe image data of the subject using the calibration model. In someembodiments, when the target image data of the subject is reconstructed,the calibration model may be used for forward and backward projection onthe image data, which may improve accuracy of the target image data ofthe subject.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations and modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure. In someembodiments, one or more operations may be omitted and/or one or moreadditional operations may be added. For example, operation 810 andoperation 820 may be combined into a single operation. As anotherexample, one or more other optional operations (e.g., a storingoperation) may be added elsewhere in the process 800. In the storingoperation, the processing device 120 may store information and/or data(e.g., the image data of the subject, the calibration model, the targetimage data, etc.) associated with the medical system 100 in a storagedevice (e.g., the storage device 130) disclosed elsewhere in the presentdisclosure.

FIG. 9 is a block diagram illustrating an exemplary processing deviceaccording to some embodiments of the present disclosure. In someembodiments, processing device 120 may be implemented on a computingdevice 500 (e.g., the processor 510) illustrated in FIG. 5 or a CPU 640as illustrated in FIG. 6 . As illustrated in FIG. 9 , the processingdevice 120 may include an acquisition module 910, a first positiondetermination module 920, a second position determination module 930,and a model determination module 940. Each of the modules describedabove may be a hardware circuit that is designed to perform certainactions, e.g., according to a set of instructions stored in one or morestorage media, and/or any combination of the hardware circuit and theone or more storage media.

The acquisition module 910 may be configured to obtain image data of areference object acquired by an imaging device scanning the referenceobject. The reference object may include a support (e.g., the support1210) and multiple elements (e.g., the element 1220) arranged on thesupport. The image data of the reference object may includerepresentations of at least six elements among the multiple elements ofthe reference object. In some embodiments, the image data of thereference image may be acquired by one of the plurality of radiationsources emitting radiation beams to scan the reference object. In someembodiments, the image data of the reference image may be acquired by atleast a portion of the plurality of radiation sources emitting radiationbeams to scan the reference object.

The first position determination module 920 may be configured todetermine a first position of each of the at least six elements in theimage data. Based on the image data of the reference object, the firstposition determination module 920 may determine the first position ofeach of the at least six elements in the image data. In someembodiments, the first position determination module 920 may determinethe first position of each of the at least six elements from the imagedata using an identification technique (e.g., an image segmentationtechnique, a machine learning technique, etc.). In some embodiments, thefirst position determination module 920 may establish a coordinatesystem (also referred to as a first coordinate system) for the imagedata. The first coordinate system may also be referred to as an imagecoordinate system. In some embodiments, the first position determinationmodule 920 may establish a coordinate system based on the projectionplane of the radiation source on the detector (e.g., the detector 1320).The first coordinate system may include a rectangular plane coordinatesystem, a planar polar coordinate system, etc. In some embodiments, thecoordinate system may be located in any plane parallel to the projectionplane of the detector and an origin of the coordinate system may be anypoint in the plane.

The second position determination module 930 may be configured todetermine a second position of each of the at least six elements in aspace where the imaging device is arranged. In some embodiments, thesecond position of each of the at least six elements in the space may bedenoted by a space coordinate system. The space coordinates system mayalso be referred to as a second coordinate system. In some embodiments,the second coordinate system may be a default setting of the imagingsystem 100. In some embodiments, the second position determinationmodule 930 may establish the second coordinate system. The secondposition determination module 930 may determine any point as an originof the second coordinate system. In some embodiments, the origin of thespace coordinates system may be determined based on a default setting ofthe imaging device or by a user (e.g., a doctor, a technician, anoperator, etc.). After establishing the second coordinate system,coordinates of a position of each component of the imaging device in thespace may be determined according to a structure of the imaging device.

The model determination module 940 may be configured to determine, basedon the first position and the second position, a calibration model. Thecalibration model may indicate a transform relationship between aposition of each pixel in the image data and a position of a portion ofa subject (e.g., an element in the reference object) represented by thepixel in a space. In some embodiments, the model determination module940 may determine, based on the first position and the second position,and the arrangement of the at least six elements in the referenceobject, multiple pairs of positions. Each of the multiple pairs ofpositions may include the first position and the second position of thesame element among the at least six elements. Since the image data ofthe reference object is generated after the radiation beams emitted fromone or more of the plurality of radiation sources to the referenceobject, there may be a correspondence between the first position of eachof the at least six elements in the image data and the second positionof each of the at least six elements in the space. In some embodiments,when the at least six elements represented in the image data arearranged in one single layer in the reference object, the modeldetermination module 940 may determine the first position and the secondposition of the same element in the at least six elements based on anarrangement position in the at least six elements in the image data andan arrangement position in the at least six elements in the referenceobject (or in the space). In some embodiments, when the at least sixelements represented in the image data are arranged in two or morelayers in the reference object, the model determination module 940 maydetermine the first position and the second position of the same elementin the at least six elements based on an arrangement position in the atleast six elements in the image data, an arrangement position in the atleast six elements in the reference object (or in the space), and a sizeof an element in the image data. In some embodiments, for a radiationsource, the first position of each of the at least six elements in theimage data may be determined. Based on the correspondence between thefirst position and the second position, the second position of theelement in the space may be determined. The first position and thesecond position of the same element may be determined as a pair ofpositions. In some embodiments, the model determination module 940 maydetermine, based on the multiple pairs of positions, the calibrationmodel. Since both the first position and the second position of the sameelement among the at least six elements are represented as coordinates,the correspondence between a pair of the first position and the secondposition may be represented as a set of equations. More descriptionsregarding the determination of the calibration model may be found inFIG. 8 and the descriptions thereof.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. Apparently, for persons having ordinary skills inthe art, multiple variations and modifications may be conducted underthe teachings of the present disclosure. However, those variations andmodifications do not depart from the scope of the present disclosure.For example,

FIG. 10 is a schematic diagram illustrating an exemplary process fordetermining a calibration model according to some embodiments of thepresent disclosure. In some embodiments, process 1000 may be implementedas a set of instructions (e.g., an application) stored in the storagedevice 130, storage 520, or storage 690. The processing device 120, theprocessor 510, and/or the CPU 640 may execute the set of instructions,and when executing the instructions, the processing device 120, theprocessor 510, and/or the CPU 640 may be configured to perform theprocess 1000. The operations of the illustrated process presented beloware intended to be illustrative. In some embodiments, the process 1000may be accomplished with one or more additional operations not describedand/or without one or more of the operations discussed. Additionally,the order of the operations of the process 1000 illustrated in FIG. 10and described below is not intended to be limiting. In some embodiments,the calibration model described in connection with operation 820 in FIG.8 may be obtained according to the process 1000.

In 1010, the processing device 120 (e.g., the acquisition module 910)may obtain image data of a reference object acquired by an imagingdevice scanning the reference object.

The reference object may include a support (e.g., the support 1210) andmultiple elements (e.g., the element 1220) arranged on the support. Eachof the multiple elements may include a material with an attenuationcoefficient being different from an attenuation coefficient of amaterial of the support. More descriptions regarding the referenceobject may be found in FIG. 12 and the descriptions thereof.

In some embodiments, the imaging device (e.g., the medical device 110,the medical device 200, the medical device 300) may include a pluralityof radiation sources and a detector. In some embodiments, when thereference object is located at a detection region of the imaging device,each of the plurality of radiation sources may cover at least a portionof the multiple elements in the reference object. In some embodiments, acenter point of the reference object located at the detection region maybe aligned with a center point of the plurality of radiation sources inthe vertical direction. As used herein, a radiation source covering atleast a portion of the multiple elements refers to that the at least aportion of the multiple elements are on transmission paths of radiationbeams emitted by the radiation source. In other words, a radiationsource covering at least a portion of the multiple elements refers tothat the at least a portion of the multiple elements are in a radiationfield of the radiation source. In some embodiments, the count (ornumber) of the at least a portion of the multiple elements covered bythe radiation source may exceed or equal 6. Among the at least a portionof the multiple elements covered by the radiation source, each twoelements in at least six elements may be not on the same transmissionpath of a radiation beam emitted by the radiation source. Moredescriptions regarding the imaging device may be found in FIGS. 1-3 andthe descriptions thereof.

The image data of the reference object may include representations of atleast six elements among the multiple elements of the reference object.Each of the at least six elements may be represented in the image dataas a point. Positions of at least six elements represented in the imagedata may be different or not overlapped. As used herein, a position ofan element represented in the image data refers to a position of acenter of a point that represents the element.

In some embodiments, the image data of the reference image may beacquired by one of the plurality of radiation sources emitting radiationbeams to irradiate the reference object. For instance, the plurality ofradiation sources may be controlled to emit radiation beams successivelyto scan the reference object. The image data of the reference objectcorresponding to each of the plurality of radiation sources may beobtained independently. Referring to FIG. 11 , FIG. 11 is a schematicdiagram illustrating imaging based on a planar array radiation sourceaccording to some embodiments of the present disclosure. As shown inFIG. 11 , the planar array radiation source 1110 may include a pluralityof radiation sources, such as radiation source 1111, a radiation source1112, etc. The plurality of radiation sources in the planar arrayradiation source 1110 may be arranged at intervals on a plane (e.g., asupport) parallel to a surface of a detector 1120. It should be notedthat the planar array radiation source 1110 in FIG. 11 is an example,which may not be limited herein. For example, the plurality of radiationsources may be arranged at intervals on a curved surface parallel to thesurface of the detector 1120. One or more radiation sources in theplanar array radiation source 1110 may be controlled to emit theradiation beams successively to scan the reference object. For example,a position and a count of the radiation sources in the planar arrayradiation source 1110 may be controlled according to information of thereference object. As another example, a radiation dose of the radiationsource in the planar array radiation source 1110 may be controlledaccording to an imaging requirement (e.g., a treatment planning, atreatment protocol, etc.). As still another example, the radiation doseof the radiation source may be controlled according to a requirement ofimage quality.

In some embodiments, each of the plurality of radiation sources maycorrespond to a projection region on the detector. As used herein, aradiation source corresponding to a projection region on the detectorrefers to that radiation beams emitted by the radiation source arereceived by detecting units in the projection region. As shown in FIG.11 , region 1130 is a projection region corresponding to the radiationsource 1111, and region 1140 is a projection region corresponding to theradiation source 1112. When a radiation source (e.g., the radiationsource 1111, the radiation source 1112, etc.) emits the radiation beams,detecting units in a corresponding projection region (e.g., the region1130, the region 1140, etc.) may receive or detect radiation beams thatpass through a portion of the reference object to generate the imagedata of the portion of the reference object. The image data of theportion of the reference object may represent elements in the portion ofthe reference object. In some embodiments, projection regionscorresponding to two adjacent radiation sources may be overlappedpartially. In other words, a portion of a projection regioncorresponding to one of the two adjacent radiation sources may be thesame as a portion of a projection region corresponding to another one ofthe two adjacent radiation sources. In some embodiments, an overlappingprojection region may be formed between adjacent radiation sources, suchas an overlapping region 1150 formed between the region 1130 and theregion 1140 in FIG. 11 . During scanning the reference object, theplurality of radiation sources in the planar array radiation source 1110may be controlled to emit the radiation beams successively to scan thereference object. Therefore, the image data of the reference objectcorresponding to each of the plurality of radiation sources may beobtained. That is, each of the plurality of radiation sources mayrespectively emit the radiation beams (e.g., X-rays) to irradiate thereference object to obtain the image data of the reference object.

In some embodiments, the image data of the reference image may beacquired by at least a portion of the plurality of radiation sourcessimultaneously emitting radiation beams to irradiate the referenceobject. For example, multiple radiation sources among which each twoadjacent radiation sources that do not include an overlapping projectionregion may simultaneously emit radiation beams to irradiate thereference object, thereby reducing scanning time. It should be notedthat if the multiple radiation sources are controlled to simultaneouslyemit the radiation beams, the projection regions corresponding to themultiple radiation sources may do not correspond to an overlappingprojection region on the detector. That is, if two radiation sourcesinclude an overlapping projection region on the detector, the tworadiation sources may be controlled to emit the radiation beams toirradiate the reference object separately. During an actual scanning,the plurality of radiation sources in the imaging device may be dividedinto one or more groups according to the above principle. The radiationsources in the same group may be configured to generate and emitradiation beams for scanning at the same time. Alternatively, theplurality of radiation sources in the imaging device may be used to scanthe reference object successively at intervals.

After a radiation source in the planar array radiation source 1110 emitsthe radiation beams to the reference object, energy of the radiationbeams may attenuate in the reference object, pass through the referenceobject, and be projected on the detector 1120. The detector 1310 mayreceive or detect radiation particles (e.g., neutrons, protons,electrons, p-mesons, heavy ions, X-ray, y-ray, ultraviolet, laser, etc.)in the radiation beams to obtain the image data of the reference objectcorresponding to the radiation source.

In 1020, the processing device 120 (e.g., the first positiondetermination module 920) may determine a first position of each of theat least six elements in the image data.

Based on the image data of the reference object, the processing device120 (e.g., the first position determination module 920) may determinethe first position of each of the at least six elements in the imagedata. In some embodiments, the first position of each of the at leastsix elements may be determined from the image data using anidentification technique (e.g., an image segmentation technique, amachine learning technique, etc.). For example, the processing device120 may process the image data of the reference object based on an imagesegmentation technique to determine the first position of each of the atleast six elements. Exemplary image segmentation techniques may includea region-based segmentation, an edge-based segmentation, a wavelettransform segmentation, a mathematical morphology segmentation, anartificial neural network-based segmentation, a genetic algorithm-basedsegmentation, or the like, or a combination thereof. As another example,the processing device 120 may process the image data of the referenceobject based on a trained machine learning model (also referred to as aposition determination model). In some embodiments, the processingdevice 120 may retrieve the position determination model from a storagedevice (e.g., the storage device 130, the terminals(s) 140, or any otherstorage device) to process the image data of the reference object. Forexample, the position determination model may be determined by traininga machine learning model offline based on a plurality of trainingsamples using the processing device 120 or a processing device otherthan the processing device 120. The position determination model may bestored in the storage device 130, the terminals(s) 140, or any otherstorage device. For instance, the processing device 120 may retrieve theposition determination model from the storage device 130, theterminals(s) 140, or any other storage device in response to receipt ofa request for determining the first position of each of the at least sixelements in the image data. In some embodiments, the processing device120 may input the image data of the reference object into the positiondetermination model. An output result may be generated by the positiondetermination model. The output result of the position determinationmodel may include the first position of each of the at least sixelements in the image data.

In some embodiments, the processing device 120 may establish acoordinate system (also referred to as a first coordinate system) forthe image data. The first coordinate system may also be referred to asan image coordinate system. In some embodiments, the processing device120 may establish the first coordinate system based on the projectionplane of the radiation source on the detector (e.g., the detector 1320).The first coordinate system may include a rectangular plane coordinatesystem, a planar polar coordinate system, etc. In some embodiments, thefirst coordinate system may be located in any plane parallel to theprojection plane of the detector and an origin of the first coordinatesystem may be any point in the plane. For example, the processing device120 may establish a rectangular plane coordinate system in theprojection plane of the detector and set a midpoint of the projectionplane as the origin of the rectangular plane coordinate system.Therefore, the first position of each of the at least six elements inthe image data may be represented by the first coordinate system astwo-dimension coordinates, such as (u_(i), v_(i)).

In 1030, the processing device 120 (e.g., the second positiondetermination module 930) may determine a second position of each of theat least six elements in a space where the imaging device is arranged.

In some embodiments, the second position of each of the at least sixelements in the space may be denoted by a space coordinate system. Thespace coordinates system may also be referred to as a second coordinatesystem. In some embodiments, the second coordinate system may be adefault setting of the imaging system 100. For example, the secondcoordinate system may be set when the imaging device is mounted. In someembodiments, the processing device 120 may establish the secondcoordinate system. The processing device 120 may determine any point asan origin of the second coordinate system. For example, a midpoint ofthe plurality of radiation sources of the imaging device (e.g., theplanar array radiation source 1110) may be determined as the origin ofthe space coordinates system. In some embodiments, the origin of thesecond coordinate system may be determined based on a default setting ofthe imaging device or by a user (e.g., a doctor, a technician, anoperator, etc.). After establishing the second coordinate system,coordinates of a position of each component of the imaging device in thespace may be determined according to a structure of the imaging device.For example, the coordinates of each radiation source and the detectormay be determined. As another example, the second position of eachelement of the reference object in the space may be determined asthree-dimension coordinates such as (x_(i), y_(i), z_(i)).

In some embodiments, the imaging device under the space coordinatesystem may be displayed on a screen of a terminal (e.g., the terminal140). Coordinates of the position of each component of the imagingdevice may be displayed directly or through clicked by a mouse.

In 1040, the processing device 120 (e.g., the model determination module940) may determine, based on the first position and the second position,a calibration model. In some embodiments, the imaging device may includea plurality of radiation sources and each of the plurality of radiationsources may be configured to generate and emit radiation beams forscanning the reference object to obtain the image data. The calibrationmodel determined based on the image data may correspond to one of theplurality of radiation sources that is used in the acquisition of theimage data.

The calibration model may indicate a transform relationship between aposition of each pixel in the image data and a position of a portion ofa subject (e.g., an element in the reference object) represented by thepixel in a space.

In some embodiments, the processing device 120 may determine, based onthe first position and the second position, and characteristics of theat least six elements in the reference object, multiple pairs ofpositions. Each pair of the multiple pairs of positions may include thefirst position and the second position of the same element among the atleast six elements. In some embodiments, the characteristics of the atleast six elements in the reference object may include an arrangementposition of each element, a size of each element, a material of eachelement, etc. In some embodiments, each of the at least six elements maybe represented in the image data as a point. The first position of anelement in the image data may also be referred to as a first position ofa point representing the element in the image data. The processingdevice 120 may determine the multiple pairs of positions by matchingpoints and the at least six elements. A point matching an element refersto that the point in the image data representing the element.

In some embodiments, when the at least six elements represented in theimage data are arranged in one single layer in the reference object, theprocessing device 120 may match the points and the at least six elementsbased on arrangement positions of the points in the image data andarrangement positions of the at least six elements in the referenceobject (or in the space). The first position of a point and the secondposition of an element that match the point may form a pair ofpositions. For example, if an element arranged in the at least sixelements is located at a first column and a first row, and a pointrepresented in the image data is located at a first column and a firstrow in the image data, the point and the element may be matched. Thefirst position of the point and the second position of the element maybe designated as a pair of positions. A matching relationship betweenthe first position and the second position of the same element (orbetween a point and an element) may be established. As another example,if the at least six elements include materials with differentattenuation coefficients, points representing the at least six elementsin the image data may be with different gray values. The processingdevice 120 may match the points and the at least six elements based onthe different gray values.

In some embodiments, when the at least six elements represented in theimage data are arranged in two or more layers in the reference object,the processing device 120 may match the points and the at least sixelements in the at least six elements based on arrangement positions ofthe at least six elements in the image data, arrangement positions ofthe at least six elements in the reference object (or in the space), anda size of each element in the image data. In some embodiments, the sizeof an element (e.g., diameter) represented in the image data may berelated to a distance from the element to the radiation source. Theshorter the distance from the element to the radiation source is, thegreater the size of the element represented in the image data may be. Insome embodiments, points representing elements arranged in differentlayers and aligned in the vertical direction may be closer with eachother represented in the image data than elements in the same layer. Theprocessing device 120 may determine points representing elements in thesame layer according to the sizes of points. The processing device 120may match points and elements in the same layer based on arrangementpositions of the points in the image data and arrangement positions ofthe at least six elements in the reference object (or in the space) asdescribed above.

Referring to FIG. 12-14 , FIG. 12 is a schematic diagram illustrating areference object according to some embodiments of the presentdisclosure. Elements of the reference object are arranged in two layers.Elements in the two layers may be aligned in the vertical direction.FIG. 13 is a schematic diagram illustrating an arrangement of elementsin one layer of the reference object according to some embodiments ofthe present disclosure. As shown in FIG. 13 , region 1320 corresponds toa radiation source. Elements in the region 1320 are arranged in twolayers, i.e., 32 elements. FIG. 14 is a schematic diagram illustratingan image of elements in region 1320 according to some embodiments of thepresent disclosure. The image shown in FIG. 14 was acquired by theradiation source emitting radiation beams to scan elements in region1320.

As shown in FIG. 14 , a diameter of a point 1410 representing a specificelement in the reference object as shown in FIG. 12 is larger than adiameter of the point 1420 representing another element in the referenceobject as shown in FIG. 12 . The specific element corresponding to point1410 and the another element corresponding to point 1420 may be arrangedin different layers and aligned in the vertical direction. The specificelement corresponding to the point 1410 is closer to the radiationsource than the another element corresponding to the point 1420. In someembodiments, elements with the same diameter represented in the imagedata may be arranged in the same layer or have the same distance to theradiation source in the vertical direction.

In some embodiments, for a radiation source, the first position of eachof the at least six elements in the image data may be determined. Basedon the matching relationship between the first position and the secondposition, the second position of the element in the space may bedetermined. The first position and the second position of the sameelement may be determined as a pair of positions.

In some embodiments, the processing device 120 may determine, based onthe multiple pairs of positions, the calibration model. Since both thefirst position and the second position of the same element among the atleast six elements are represented as coordinates, the correspondencebetween a pair of the first position and the second position may berepresented as a set of equations. For example, the first coordinatesystem may be a rectangular plane coordinate system located in theprojection plane of the detector and a midpoint of the projection planemay be determined as the origin of the rectangular plane coordinatesystem, and a midpoint of the planar array radiation source may bedetermined as the origin of the second coordinates system. The count ofelements represented in the image data may be represented by n. n may bea positive integer larger than or equal to 6, such as 6, 7, 8, 9, 10,12, etc. The first position of an element represented in the image datamay be represented as first coordinates (u_(i), v_(i)), and the secondposition of the element in the space may be represented as secondcoordinates (x_(i), y_(i), z_(i)). i refers to a serial number of theelement. i may be a positive integer. For each of the elements, thecorrespondence between the first coordinates and the second coordinatesmay be represented as the following Equations (1), (2), and (3):

u _(i) w _(i) =p ₁₁ x _(i) +p ₁₂ y _(i) +p ₁₃ z _(i) +p ₁₄,  (1)

v _(i) w _(i) =p ₂₁ x _(i) +p ₂₂ y _(i) +p ₂₃ z _(i) +p ₂₄,  (2)

w _(i) =p ₃₁ x _(i) +p ₃₂ y _(i) +p ₃₃ z _(i) +p ₃₄.  (3)

By calculating the above Equations (1), (2) and (3) (e.g., Equation(1)−Equation (3)×u_(i) and Equation (2)−Equation (3)×v_(i)), thefollowing Equations (4) and (5) may be obtained.

p ₁₁ x _(i) +p ₁₂ y _(i) +p ₁₃ z _(i) +p ₁₄ −u _(i)(p ₃₁ x _(i) +p ₃₂ y_(i) +p ₃₃ z _(i) +p ₃₄)=0,  (4)

p ₂₁ x _(i) +p ₂₂ y _(i) +p ₂₃ z _(i) +p ₂₄ −v _(i)(p ₃₁ x _(i) +p ₃₂ y_(i) +p ₃₃ z _(i) +p ₃₄)=0.  (5)

Based on the elements corresponding to the radiation source, an Equation(6) may be obtained.

$\begin{matrix}{{{AP} = 0.}{{where},{A = {\begin{bmatrix}x_{1} & y_{1} & z_{1} & 1 & 0 & 0 & 0 & 0 & {{- u_{1}}x_{1}} & {{- u_{1}}y_{1}} & {{- u_{z}}z_{1}} & {- u_{1}} \\0 & 0 & 0 & 0 & x_{1} & y_{1} & z_{1} & 1 & {{- v_{1}}x_{1}} & {{- v_{1}}y_{1}} & {{- v_{1}}z_{1}} & {- v_{1}} \\x_{2} & y_{2} & z_{2} & 1 & 0 & 0 & 0 & 0 & {{- u_{2}}x_{2}} & {{- u_{2}}y_{2}} & {{- u_{2}}z_{2}} & {- u_{2}} \\0 & 0 & 0 & 0 & x_{2} & y_{2} & z_{2} & 1 & {{- v_{2}}x_{2}} & {{- v_{2}}y_{2}} & {{- v_{2}}z_{2}} & {- v_{2}} \\ \vdots & \vdots & \vdots & \vdots & \vdots & \vdots & \vdots & \vdots & \vdots & \vdots & \vdots & \vdots \\x_{n} & y_{n} & z_{n} & 1 & 0 & 0 & 0 & 0 & {{- u_{n}}x_{n}} & {{- u_{n}}y_{n}} & {{- v_{n}}z_{n}} & {- u_{n}} \\0 & 0 & 0 & 0 & x_{n} & y_{n} & z_{n} & 1 & {{- v_{n}}x_{n}} & {{- v_{n}}y_{n}} & {{- v_{n}}z_{n}} & {- v_{n}}\end{bmatrix}.}}}} & (6)\end{matrix}$

Matrix A may be determined based on each pair of the multiple pairs ofpositions. Since the first coordinates and the second coordinates of theelements are determined, the matrix A may be obtained. The matrix P maybe determined by solving Equation (6) through a mathematical manner.

P=(p ₁₁ ,p ₁₂ ,p ₁₃ ,p ₁₄ ,p ₂₁ ,p ₂₂ ,p ₂₃ ,p ₂₄ ,p ₃₁ ,p ₃₂ ,p ₃₃ ,p₃₄).  (7)

The mathematical manner may include a singular value decomposition (SVD)manner, an elimination manner, a Cramer principle, a generalized inversematrix law, a direct triangle law, a square root manner, a pursuitmanner, or the like.

The matrix P may be the calibration model, which represents thetransform relationship between the position of each pixel in the imagedata and the position of a portion of the subject represented by thepixel in the space. In some embodiments, for each radiation source, theprocessing device 120 may determine a corresponding calibration model.Therefore, a count of calibration models may be same as a count of theradiation sources. Since there are 12 unknowns in the matrix P accordingto the Equation (7), 12 equations may be needed to solve the matrix P.To obtain 12 equations, at least 6 elements may be obtained. Inaddition, the at least 6 elements represented in the image data may notoverlap. That is, the first positions of six elements in the at leastsix elements may be different.

If the count of elements represented in the image data is less than 6,the count of different first positions may be determined less than 6,and the equations may not be solved to obtain matrix P.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations and modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure. In someembodiments, one or more operations may be omitted and/or one or moreadditional operations may be added. For example, operation 1010 andoperation 1020 may be combined into a single operation. As anotherexample, one or more other optional operations (e.g., a storingoperation) may be added elsewhere in the process 1000. In the storingoperation, the processing device 120 may store information and/or data(e.g., the image data of the reference object, the calibration model,the first position, the second position, etc.) associated with themedical system 100 in a storage device (e.g., the storage device 130)disclosed elsewhere in the present disclosure.

FIG. 12 is a schematic diagram illustrating an exemplary referenceobject according to some embodiments of the present disclosure. As shownin FIG. 12 , the reference object may include a support 1210 andmultiple elements 1220 arranged on the support 1210. The multipleelements 1220 may be spaced apart from each other.

A projection region of each radiation source may correspond to at leastsix elements 1220 and projection positions of each two or more elementsamong at least six elements 1220 in a projection plane of the radiationsource may be not overlapped on a projection plane when radiation beamsemitted by a radiation source in a planar array radiation source passthrough the reference object. As used herein, the projection region of aradiation source refers to a region on the projection plane that theradiation beams emitted by the radiation source are projected on alongprojection directions. The projection plane refers to a plane on thedetector where the radiation beams emitted by the radiation source areprojected on along projection directions. The projection direction of aradiation beam may also be referred to as a transmission direction orpath of the radiation beam. Each two or more elements among at least sixelements 1220 may be not located at the same projection direction ortransmission path.

A detector (e.g., the detector 232, the detector 310, the detector 1120)may receive radiation beams generated by the radiation source afterpassing through the reference object.

In some embodiments, each of the multiple elements 1220 may include amaterial with an attenuation coefficient being different from anattenuation coefficient of a material of the support 1210. Therefore, adifference between the attenuation coefficient of the element 1220 andthe attenuation coefficient of the support 1210 may be used todistinguish a projection difference between the element 1220 and thesupport 1210, so that the element 1220 and the support 1210 may bedistinguished in a projection image. In some embodiments, theattenuation coefficient of the element 1220 may be greater than theattenuation coefficient of the support 1210. Alternatively, theattenuation coefficient of the element 1220 may be smaller than theattenuation coefficient of the support 1210. As shown in FIG. 12 , theattenuation coefficient of the element 1220 is greater than theattenuation coefficient of the support 1210. For example, the materialof the support 1210 may include PMMA, and the material of the element1220 may include steel.

In some embodiments, the attenuation coefficient of the multipleelements 1220 may be different. At least a portion of the multipleelements represented in a projection image may be distinguished fromeach other based on gray values of the elements represented in theprojection image. In some embodiments, the attenuation coefficients ofthe multiple elements 1220 may be the same. At least a portion of themultiple elements 1220 represented in a projection image may bedistinguished from each other based on an arrangement and/or sizes ofthe elements 1220 in the reference object. A shape of the element 1220may include a sphere, a cylinder, a prism, a cube, etc. The specificshape of the element 1220 may not be limited in the embodiment. Merelyby way of example, the shape of the element 1220 may be a sphere.

In some embodiments, the multiple elements 1220 may be distributed inlayers on the support 1210. For example, the multiple elements 1220 maybe arranged in one single layer in the support 1210. As another example,the multiple elements 1220 may be arranged in a plurality of layers(e.g., two layers, three layers, four layers, etc.) in support 1210. Acount of layers of the multiple elements 1220 may not be limited, aslong as the count of elements 1220 corresponding to each radiationsource is greater than or equal to six.

In some embodiments, the multiple elements 1220 may be arranged in atleast two layers in the support 1210. Each two or more elements amongthe at least six elements may not be located at the same projectiondirection of the radiation beams emitted by the radiation source. Insome embodiments, as shown in FIG. 12 , the multiple elements 1220 maybe arranged in two layers. Two elements located in different layers maybe aligned in the vertical direction such that the two elements may benot located at the same projection direction or transmission path of aradiation beam of the radiation source. The count (or number) ofelements 1220 in each layer may be determined by the count of radiationsources. The count of elements 1220 covered by each radiation source maybe the same, and the elements 1220 may not be overlapped on a projectiondirection of the radiation beams emitted by the radiation source. Insome embodiments, the radiation source and a center point of a regionincluding the at least six elements corresponding to or covered by theradiation source may be aligned in a vertical direction, which mayensure the accuracy of the measurement.

In some embodiments, elements in the same layer may be arranged atintervals. In some embodiments, elements covered by different radiationsources may be different or not overlapped. In other words, each elementcovered by a radiation source may be different from each element coveredby another radiation source. In some embodiments, elements covered bydifferent radiation sources may be overlapped at least in part. In otherwords, one or more elements covered by a radiation source may be coveredby another radiation source. For example, FIG. 13 is a schematic diagramillustrating an arrangement of elements in one layer of the referenceobject in FIG. 12 according to some embodiments of the presentdisclosure. The multiple elements 1320 may be distributed in rows andcolumns. As shown in FIG. 13 , elements in a dashed frame 1310 areelements covered by the radiation source 1111, and elements in a solidframe 1320 are elements covered by the radiation source 1112. A portionof elements in the dashed frame 1310 are overlapped with a portion ofelements in the solid frame 1320.

In some embodiments, the arrangement of the elements covered bydifferent radiation sources may be different or the same. For example,an interval between two elements among the elements corresponding to aradiation source may be different from or the same as an intervalbetween two elements among the elements corresponding to anotherradiation source. As another example, the elements corresponding to aradiation source may be arranged in one single layer and the elementscorresponding to another radiation source may be arranged in two or morelayers.

In some embodiments, a distance (i.e., interval) between two adjacentelements among the plurality of elements may be determined based on afirst distance between the planar array radiation source and/or thereference object and a second distance between the planar arrayradiation source and the detector. The greater the first distancebetween the planar array radiation source and the reference object, thegreater the distance between two adjacent elements may be. The greaterthe second distance between the planar array radiation source and thedetector, the greater the distance between two adjacent elements may be.For example, since it is necessary to distinguish which radiation sourceis the projection of the radiation source in the projection image, acertain distance must be maintained between multiple elements. Thedistance between the multiple elements may be determined according tothe first distance between the planar array radiation source and thereference object, and the second distance between the planar arrayradiation source and the detector, as long as the separation distancemay ensure that the projections of the elements do not overlap on thedetector.

FIG. 15 is a block diagram illustrating another exemplary processingdevice according to some embodiments of the present disclosure. In someembodiments, processing device 120 may be implemented on a computingdevice 500 (e.g., the processor 510) illustrated in FIG. 5 or a CPU 640as illustrated in FIG. 6 . As illustrated in FIG. 15 , the processingdevice 120 may include an acquisition module 1510, a scatter componentsdetermination module 1520, and a scatter correction module 1530. Each ofthe modules described above may be a hardware circuit that is designedto perform certain actions, e.g., according to a set of instructionsstored in one or more storage media, and/or any combination of thehardware circuit and the one or more storage media.

The acquisition module 1510 may be configured to obtain first image dataof a subject acquired by an imaging device when a beam stop array isarranged on a path of radiation beams emitted by the radiation sourcefor one of at least a portion of a plurality of radiation sources. Theimaging device may include the plurality of radiation sources and adetector. The plurality of radiation sources may be arranged to form aplanar array radiation source. The one of the at least a portion of theplurality of radiation sources may correspond to a beam stop array. Thebeam stop array may be configured to block radiation beams emitted theradiation source in the plurality of radiation sources when the beamstop array is arranged on the path (also referred to as a transmissionpath) of radiation beams emitted by the radiation source. In someembodiments, the acquisition module 1510 may control the beam stop arraycorresponding to the radiation source to move from a first position to asecond position to block radiation beams emitted from the radiationsource. The first position may be a position where the beam stop arrayis located such that radiation beams emitted the radiation source arenot blocked. The second position may be a position where the beam stoparray is located such that at least a portion of the radiation beamsemitted from the radiation source are blocked. The first image data ofthe subject may include scatter components of the subject that areformed when the beam stop array is arranged on the path of radiationbeams emitted by the radiation source. In some embodiments, theacquisition module 1510 may obtain the first image data formed by theradiation beams passing through the support of the beam stop array andthe subject. In some embodiments, when the beam stop array is arrangedon the path of radiation beams emitted by the radiation source, theradiation source may include a first radiation parameter. In someembodiments, the acquisition module 1510 may obtain the first image datafrom the imaging device (e.g., the medical device 110, the medicaldevice 300). In some embodiments, the acquisition module 1510 may obtainthe first image data of the subject from a storage device, for example,the storage device 130, or any other storage.

The acquisition module 1510 may be further configured to obtain secondimage data of the subject acquired by the imaging device when the beamstop array is not arranged on the path of radiation beams emitted by theradiation source for one of the at least a portion of the plurality ofradiation sources. In some embodiments, the acquisition module 1510 maycontrol the beam stop array corresponding to the radiation source tomove from the second position to the first position such that the beamstop array is not arranged on the path of radiation beams emitted by theradiation source. The second image data of the subject may include arepresentation of the subject that is formed when the beam stop array isnot arranged on the path of radiation beams emitted by the radiationsource. When the beam stop array moves from the second position to thefirst position, the elements in the beam stop array may not block theradiation beams emitted by the radiation source. The acquisition module1510 may acquire the second image data. In some embodiments, when thebeam stop array is not arranged on the path of radiation beams emittedby the radiation source, the radiation source may include a secondradiation parameter. In some embodiments, the acquisition module 1510may obtain the second image data from the imaging device (e.g., themedical device 110, the medical device 300). In some embodiments, theacquisition module 1510 may obtain the second image data of the subjectfrom a storage device, for example, the storage device 130, or any otherstorage.

The scatter components determination module 1520 may be configured todetermine, based on the first image data, a scatter distributionassociated with the subject included in the second image data for one ofthe at least a portion of the plurality of radiation sources. In someembodiments, the scatter components determination module 1520 maydetermine, based on the first image data, the scatter distributionassociated with the subject included in the second image data. Since thefirst image data of the subject is acquired when the beam stop array isarranged on the path of radiation beams emitted from the radiationsource, a region of the subject that is located on a path of radiationbeams where the elements of the beam stop array are located may be notirradiated by the radiation beams that are blocked by the elements ofthe beam stop array. Therefore, image data corresponding to the regionof the subject that is located on the path of radiation beams where theelements of the beam stop array are located in the first image data maybe generated as the scattering of the subject when the radiation beamspass through the support of the beam stop array to irradiate thesubject. In some embodiments, the scatter components determinationmodule 1520 may designate the image data corresponding to the region ofthe subject that is located on the path of radiation beams where theelements of the beam stop array are located in the first image data asscatter distribution associated with the region of the subject includedin the first image data. Since the scatter distribution associated withthe region of the subject is a partial sparse sampling, the scattercomponents determination module 1520 may perform an interpolationoperation on the scatter distribution associated with the region of thesubject to obtain the scatter distribution associated with the subjectincluded in the first image data. In some embodiments, the scatterdistribution may be proportional to the radiation dose. The scattercomponents determination module 1520 may further determine the scatterdistribution associated with the subject included in the second imagedata based on the first image data (or the interpolated scatterdistribution), the first radiation dose, and the second radiation dose.In some embodiments, the p scatter components determination module 1520may determine a ratio of the second radiation dose of the second imagedata and the first radiation dose of the first image data. Subsequently,the scatter components determination module 1520 may determine thescatter distribution associated with the subject included in the secondimage data based on the first image data (or the interpolated scatterdistribution) and the ratio of the second radiation dose of the secondimage data and the first radiation dose of the first image data. Moredescriptions regarding the determination of the scatter distributionassociated with the subject included in the second image data may befound in FIG. 16 and the descriptions thereof.

The scatter correction module 1530 may be configured to determine, basedon the scatter distribution associated with the subject included in thesecond image data and the second image data, third image data of thesubject corresponding to each of the at least a portion of the pluralityof radiation sources for one of the at least a portion of the pluralityof radiation sources. The third image data of the subject correspondingto each of the at least a portion of the plurality of radiation sourcesrefers to image data of the subject after scatter correction. In someembodiments, the third image may be determined based on the scatterdistribution associated with the subject included in the second imagedata and the second image. In some embodiments, the scatter correctionmodule 1530 may determine, based on the scatter distribution and thesecond image data, the third image data. In some embodiments, theplurality of radiation sources may include a target portion in whicheach radiation source is not configured with a beam stop array. For theradiation source in the target portion (also referred to as a targetradiation source), the scatter correction module 1530 may obtain fourthimage data of the subject acquired by the target radiation sourceemitting radiation beams to scanning the subject. The fourth image dataof the subject may include a scatter distribution of the subject that isgenerated under a radiation dose corresponding to the target radiationsource. As used herein, the scatter distribution included in the fourthimage data of the subject acquired based on the target radiation sourcemay also be referred to as an estimated scatter distribution. In someembodiments, the scatter correction module 1530 may determine theestimation scanner distribution included in the fourth image datacorresponding to the target radiation source based on one or morescatter distributions included in second image data corresponding to oneor more reference radiation sources. In some embodiments, the scattercorrection module 1530 may determine the estimated scatter distributionincluded in the fourth image data by performing an interpolationoperation on the one or more scatter distributions included in thesecond image data corresponding to the reference radiation sources. Insome embodiments, the scatter correction module 1530 may determine theestimation scatter distribution included in the fourth image datacorresponding to the target radiation source based on the first imagedata corresponding to one or more reference radiation sources. In someembodiments, after determining the third image data of the subjectcorresponding to each of the plurality of radiation sources, the scattercorrection module 1530 may determine, based on the third image data ofthe subject corresponding to each of the plurality of radiation sources,target image data of the subject. In some embodiments, the scattercorrection module 1530 may perform a reconstruction operation on thethird image data of the subject corresponding to the plurality ofradiation sources. In some embodiments, the scatter correction module1530 may perform a reconstruction operation on the third image data ofthe subject corresponding to the plurality of radiation sources usingthe calibration model as described elsewhere in the present disclosure.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. Apparently, for persons having ordinary skills inthe art, multiple variations and modifications may be conducted underthe teachings of the present disclosure. However, those variations andmodifications do not depart from the scope of the present disclosure.For example, the scatter correction module 1530 and the scattercomponents determination module 1520 may be integrated into one singlemodule.

FIG. 16 is a schematic diagram illustrating an exemplary process fordetermining image data of a subject according to some embodiments of thepresent disclosure. In some embodiments, process 1600 may be implementedas a set of instructions (e.g., an application) stored in the storagedevice 130, storage 520, or storage 690. The processing device 120, theprocessor 510, and/or the CPU 640 may execute the set of instructions,and when executing the instructions, the processing device 120, theprocessor 510, and/or the CPU 640 may be configured to perform theprocess 1600. The operations of the illustrated process presented beloware intended to be illustrative. In some embodiments, the process 1600may be accomplished with one or more additional operations not describedand/or without one or more of the operations discussed. Additionally,the order of the operations of the process 1600 illustrated in FIG. 16and described below is not intended to be limiting.

In 1610, for one of at least a portion of a plurality of radiationsources, the processing device 120 (e.g., the acquisition module 1510)may obtain first image data of a subject acquired by an imaging devicewhen a beam stop array is arranged on a path of radiation beams emittedby the radiation source.

The subject may be biological or non-biological. For example, thesubject may include a patient, a man-made object, etc. As anotherexample, the subject may include a specific portion, organ, and/ortissue of the patient. As still another example, the subject may includea breast.

In some embodiments, the imaging device (e.g., the medical device 110,the medical device 300) may include the plurality of radiation sourcesand a detector. The plurality of radiation sources may be arranged toform a planar array radiation source (e.g., the radiation beamgeneration component 330). In some embodiments, the imaging device mayinclude a digital breast tomosynthesis (DBT) device. More descriptionsregarding the imaging device may be found in FIGS. 1, 3 , and thedescriptions thereof.

The one of the at least a portion of the plurality of radiation sourcesmay correspond to a beam stop array (e.g., the beam stop array 350). Thebeam stop array may be configured to block radiation beams emitted fromthe radiation source in the plurality of radiation sources when the beamstop array is arranged on paths (also referred to as a transmissionpaths) of radiation beams emitted by the radiation source. For example,the elements in the beam stop array with the high attenuationcoefficient may absorb energy of radiation beams irradiating theelements, such that at least a portion of the radiation beams may beblocked by the beam stop array. In some embodiments, the processingdevice 120 may control the beam stop array corresponding to theradiation source to move from a first position to a second position toblock radiation beams emitted from the radiation source. The firstposition may be a position where the beam stop array is located suchthat radiation beams emitted the radiation source are not blocked. Inother words, the first position may be not on the transmission path ofthe radiation beams emitted the radiation source. When the beam stoparray is located at the first position, the beam stop array may notblock radiation beams of the radiation source. The second position maybe a position where the beam stop array is located such that at least aportion of the radiation beams emitted from the radiation source areblocked. That is, when the beam stop array is located at the secondposition, the beam stop array may block at least a portion of theradiation beams emitted from the radiation source. In other words, thesecond position may be on the transmission paths of the radiation beamsemitted the radiation source.

In some embodiments, the beam stop array may be installed on a guiderail and moved from the first position to the second position bysliding. In some embodiments, the beam stop array may be installed on aturntable and moved from the first position to the second position byrotating. In some embodiments, each of the at least a portion of theplurality of radiation sources may correspond to a beam stop array. Theat least a portion of the plurality of radiation sources may correspondto multiple beam stop arrays. When the acquisition of the first imagedata is performed, one of the multiple beam stop arrays corresponding toeach of the at least a portion of the plurality of radiation sources maybe moved to the corresponding second position for the acquisition of thefirst image data for each of the at least a portion of the plurality ofradiation sources. In some embodiments, the at least a portion of theplurality of radiation sources may correspond to one single beam stoparray. And when the acquisition of the first image data is performed,the one single beam stop array may be moved to the corresponding secondposition for the acquisition of the first image data for each of the atleast a portion of the plurality of radiation sources. More descriptionsfor the beam stop array may be found in elsewhere in the presentdisclosure (e.g., FIGS. 3 and 4 , and the description thereof).

The first image data of the subject may include scatter components ofthe subject that are formed when the beam stop array is arranged on thepath of radiation beams emitted by the radiation source. In someembodiments, when the beam stop array moves from the first position tothe second position, the elements in the beam stop array with higherattenuation coefficients may block the radiation beams emitted by theradiation source. The elements of the beam stop array with higherattenuation coefficients may block a portion of the radiation beamsemitted by the radiation source, and a portion of the radiation beamsemitted by the radiation source may pass through the support of the beamstop array with the lower attenuation coefficient. The radiation beamspassing through the support of the beam stop array may irradiate andpass through the subject to form scattering in the subject. Theprocessing device 120 may obtain the first image data formed by theradiation beams passing through the support of the beam stop array andthe subject.

In some embodiments, when the beam stop array is arranged on the path ofradiation beams emitted by the radiation source, the radiation sourcemay include a first radiation parameter. The first radiation parametermay include a first position of the radiation source, a first radiationdose of the radiation source, etc. In some embodiments, the firstradiation parameter may be determined by a treatment planning. Thetreatment planning may be used to indicate scanning requirements of thesubject. In some embodiments, the treatment planning may include ascanning region, a radiation dose, a reference dose, an image sequence,or the like. The processing device 120 may obtain the treatment planningmanually or automatically. For example, the processing device 120 mayobtain the treatment planning manually input by a user (e.g., a doctor,a technician, an operator, etc.) through a terminal (e.g., the terminal140). For another example, the processing device 120 may automaticallydetermine (e.g., using a machine learning method) a correspondingtreatment planning based on historical data and current data of thesubject.

In some embodiments, the first image data may include first projectiondata of the subject corresponding to the radiation source acquired bythe detector when the beam stop array is arranged on the path ofradiation beams emitted by the radiation source. In some embodiments,the first image data may include a first image reconstructed based onthe first projection data. In some embodiments, the first image data mayinclude the first image reconstructed based on the first projection dataand a calibration model as described elsewhere in the present disclosure(e.g., FIG. 8 and the descriptions thereof).

In some embodiments, the first image data may be obtained from theimaging device (e.g., the medical device 110, the medical device 300).For example, the medical device 300 may acquire the first image data ofthe subject when the beam stop array is arranged on the path ofradiation beams emitted by the radiation source and transmit theacquired first image data of the subject to the processing device 120.In some embodiments, the processing device 120 (e.g., the thirdobtaining module 1610) may obtain the first image data of the subjectfrom a storage device, for example, the storage device 130, or any otherstorage. For example, the medical device 300 may acquire the first imagedata of the subject when the beam stop array is arranged on the path ofradiation beams emitted by the radiation source and store the acquiredfirst image data of the subject in the storage device. The processingdevice 120 may obtain the first image data of the subject from thestorage device.

In 1620, for one of the at least a portion of the plurality of radiationsources, the processing device 120 (e.g., the acquisition module 1510)may obtain second image data of the subject acquired by the imagingdevice when the beam stop array is not arranged on the path of radiationbeams emitted by the radiation source.

In some embodiments, the processing device 120 may control the beam stoparray corresponding to the radiation source to move from the secondposition to the first position such that the beam stop array is notarranged on the path of radiation beams emitted by the radiation source.

The second image data of the subject may include a representation of thesubject that is formed when the beam stop array is not arranged on thepath of radiation beams emitted by the radiation source. When the beamstop array moves from the second position to the first position, theelements in the beam stop array may not block the radiation beamsemitted by the radiation source. After the beam stop array moves fromthe second position to the first position, the processing device 120 mayacquire the second image data.

In some embodiments, when the beam stop array is not arranged on thepath of radiation beams emitted by the radiation source, the radiationsource may include a second radiation parameter. The second radiationparameter may include a second position of the radiation source, asecond radiation dose, etc. In some embodiments, the second radiationparameter may be determined by the treatment planning. In particular, asum of the first radiation dose and the second radiation dose may beless than a dose threshold. The dose threshold refers to a maximumradiation dose of the subject in the acquisition of image data of thesubject. For example, the dose threshold may be determined by thetreatment planning. As another example, the dose threshold may bedetermined by the user or according to a default setting of the system100. In some embodiments, the first radiation dose of the first imagedata may be less than the second radiation dose of the second imagedata.

The second image data may include second projection data of the subjectcorresponding to the radiation source acquired by the detector when thebeam stop array is not arranged on the path of radiation beams emittedby the radiation source. In some embodiments, the second image data mayinclude a second image reconstructed based on the second projectiondata. In some embodiments, the second image may be reconstructed basedon the second projection data and a calibration model as describedelsewhere in the present disclosure (e.g., FIG. 8 and the descriptionsthereof). In some embodiments, if the first image is reconstructed basedon the calibration model, the second image may be reconstructed based onthe calibration model.

In some embodiments, the second image data may be obtained from theimaging device (e.g., the medical device 110, the medical device 300).For example, the medical device 300 may acquire the second image data ofthe subject when the beam stop array is not arranged on the path ofradiation beams emitted by the radiation source and transmit theacquired second image data of the subject to the processing device 120.In some embodiments, the processing device 120 (e.g., the scattercomponents determination module 1520) may obtain the second image dataof the subject from a storage device, for example, the storage device130, or any other storage. For example, the medical device 300 mayacquire the second image data of the subject when the beam stop array isnot arranged on the path of radiation beams emitted by the radiationsource and store the acquired second image data of the subject in thestorage device. The processing device 120 may obtain the second imagedata of the subject from the storage device.

In 1630, for one of the at least a portion of the plurality of radiationsources, the processing device 120 (e.g., the scatter componentsdetermination module 1520) may determine, based on the first image data,a scatter distribution associated with the subject included in thesecond image data.

In some embodiments, the processing device 120 may determine, based onthe first image data, the scatter distribution associated with thesubject included in the second image data. Since the first image data ofthe subject is acquired when the beam stop array is arranged on the pathof radiation beams emitted from the radiation source, portions of thesubject located on paths of radiation beams where the elements of thebeam stop array are located on may be not irradiated by the radiationbeams that are blocked by the elements of the beam stop array.Therefore, regions in the first image data representing the portions ofthe subject located on the paths of radiation beams where the elementsof the beam stop array are located may be generated as the scattering ofthe subject when the radiation beams pass through the support of thebeam stop array to irradiate the subject.

In some embodiments, regions in the first image data representing theportions of the subject located on the paths of radiation beams wherethe elements of the beam stop array are located may be designated asscatter data associated with the portions of the subject included in thefirst image data. In some embodiments, the regions in the first imagedata may be represented as multiple light strips with gray valuesexceeding other regions in the first image data represented as blackstrips relative to the light strips. The processing device 120 maydetermine the scatter data in the first image data based on pixel valuesin the other regions of the first image data represented as the blackstrips. For example, the pixel values in the other regions of the firstimage data represented as the black strips may be designated as pixelvalues in the scatter data. In some embodiments, the processing device120 may perform an interpolation operation on the scatter dataassociated with the portions of the subject to obtain the scatterdistribution associated with the subject included in the second imagedata. The interpolation operation may include a polynomial interpolationoperation, a spline interpolation operation, a Lagrangian interpolationoperation, a Newton interpolation operation, a Hermitian interpolationoperation, a piecewise interpolation operation, etc.

In some embodiments, the first radiation dose may be different from thesecond radiation dose, and the scatter distribution associated with thesubject included in the second image data may be determined based on thefirst image data (or the interpolated scatter distribution), the firstradiation dose, and the second radiation dose. In some embodiments, thescatter distribution may be proportional to the radiation dose. Thegreater the radiation dose is, the greater the scatter distribution maybe. Therefore, the processing device 120 may further determine thescatter distribution associated with the subject included in the secondimage data based on the first image data (or the interpolated scatterdistribution), the first radiation dose, and the second radiation dose.In some embodiments, the processing device 120 may determine a ratio ofthe second radiation dose of the second image data and the firstradiation dose of the first image data. Subsequently, the processingdevice 120 may determine the scatter distribution associated with thesubject included in the second image data based on the scatter data (orthe interpolated scatter data) and the ratio of the second radiationdose of the second image data and the first radiation dose of the firstimage data. For example, if the first radiation dose is 1 mSV and thesecond radiation dose is 5 mSV, the ratio of the second radiation doseof the second image data and the first radiation dose of the first imagedata may be 5. The scatter distribution associated with the subjectincluded in the second image data may be determined as a product of theratio (e.g., 5) and the interpolated scatter data. In some embodiments,the interpolated scatter data may be denoted as a first matrix, and thescatter distribution associated with the subject included in the secondimage data may be denoted as a scatter matrix. The scatter matrix may beobtained by multiplying the first matrix with the ratio of the secondradiation dose of the second image data and the first radiation dose ofthe first image data.

In 1640, for one of the at least a portion of the plurality of radiationsources, the processing device 120 (e.g., the scatter correction module1530) may determine, based on the scatter distribution associated withthe subject included in the second image data and the second image data,third image data of the subject corresponding to each of the at least aportion of the plurality of radiation sources.

The third image data of the subject corresponding to each of the atleast a portion of the plurality of radiation sources refers to imagedata of the subject after scatter correction. In some embodiments, thethird image data may not include the scatter distribution associatedwith the subject included in the second image data. In some embodiments,the third image data may include third projection data that aredetermined based on the scatter distribution associated with the subjectincluded in the second image data and the second projection data. Insome embodiments, the third image data may include a third imagereconstructed based on the third projection data. In some embodiments,the third image may be reconstructed based on the third projection dataand the calibration model as described elsewhere in the presentdisclosure (e.g., FIG. 8 and the descriptions thereof). In someembodiments, the third image may be determined based on the scatterdistribution associated with the subject included in the second imagedata and the second image.

In some embodiments, the processing device 120 may determine, based onthe scatter distribution and the second image data, the third imagedata. For example, the processing device 120 may determine the thirdimage data of the subject corresponding to each of the at least aportion of the plurality of radiation sources by subtracting the scatterdistribution associated with the subject included in the second imagedata from the second image data. For example, the scatter distributionassociated with the subject included in the second image data may bedenoted as the scatter matrix, the second image data may be denoted as asecond matrix, and the third image data may be denoted as a thirdmatrix. The third image data may be obtained by performing a subtractionoperation between the first matrix from the second matrix.

In some embodiments, the plurality of radiation sources may include atarget portion in which each radiation source is not configured with abeam stop array. For the radiation source in the target portion (alsoreferred to as a target radiation source), the processing device 120 mayobtain fourth image data of the subject acquired by the target radiationsource emitting radiation beams to scanning the subject. The fourthimage data of the subject may include a scatter distribution of thesubject that is generated under a radiation dose corresponding to thetarget radiation source.

As used herein, the scatter distribution included in the fourth imagedata of the subject acquired based on the target radiation source mayalso be referred to as an estimated scatter distribution. In someembodiments, the processing device 120 may determine the estimationscanner distribution included in the fourth image data corresponding tothe target radiation source based on one or more scatter distributionsincluded in second image data corresponding to one or more referenceradiation sources. As used herein, image data of a subject correspondingto a radiation source refers to that the image data is acquired by theradiation source emitting radiation beams to irradiate the subject. Areference radiation source refers to a radiation source configured witha beam stop array. In some embodiments, the reference radiation sourceof the target radiation source may be determined according to a distancebetween the reference radiation source and the target radiation source.For example, a radiation source configured with a beam stop array whosedistance to the target radiation source in the target portion is lessthan a distance threshold (e.g., 10 millimeters, 20 millimeters, 30millimeters, 50 millimeters, 80 millimeters, 100 millimeters, etc.) maybe determined as a reference radiation source of the target radiationsource. As another example, a count of (e.g., 1, 2, 3, 4, 5, 6, 8, 10,12, etc.) radiation sources configured with the beam stop arrays closestto the target radiation source in the target portion may be determinedas the reference radiation sources of the target radiation source. Insome embodiments, a reference radiation source may be determinedaccording to a target portion of the subject irradiated by the targetradiation source in the target portion. In some embodiments, a radiationsource that irradiates the same portion of the subject with the targetradiation source may be determined as the reference radiation source ofthe target radiation source. For example, if a target portion of thesubject irradiated by the target radiation source in the target portionis a leg, the radiation source configured with the beam stop array thatirradiates at least a portion of the leg may be determined as thereference radiation source of the target radiation source.

In some embodiments, the processing device 120 may determine theestimated scatter distribution included in the fourth image data byperforming an interpolation operation on the one or more scatterdistributions included in the second image data corresponding to thereference radiation sources. The interpolation operation may include apolynomial interpolation operation, a spline interpolation operation, aLagrangian interpolation operation, a Newton interpolation operation, aHermitian interpolation operation, a piecewise interpolation operation,etc. After determining the estimated scatter distribution included inthe fourth image data, the processing device 120 may determine, based onthe fourth image data and the scatter distribution included in thefourth image data, third image data of the subject corresponding to thetarget radiation source. For example, the processing device 120 maydetermine the third image data of the subject corresponding to thetarget radiation source by subtracting the estimated scatterdistribution included in the fourth image data from the fourth imagedata.

In some embodiments, the processing device 120 may determine theestimation scatter distribution included in the fourth image datacorresponding to the target radiation source based on the first imagedata corresponding to one or more reference radiation sources. Theprocessing device 120 may determine scatter data based on the firstimage data corresponding to one or more reference radiation sources. Theprocessing device 120 may determine the estimation scatter datacorresponding to the fourth image data by interpolating the scatter datacorresponding to one or more reference radiation sources. The processingdevice 120 may perform an interpolating operation on the estimationscatter data corresponding to the fourth image data to obtaininterpolated estimation scatter data corresponding to the fourth imagedata. The processing device 120 may determine the estimation scatterdistribution included in the fourth image data by multiplying theinterpolated estimation scatter data with a ratio of a radiation dosecorresponding to the target radiation source and a radiation dosecorresponding to a reference radiation source.

In some embodiments, after determining the third image data of thesubject corresponding to each of the plurality of radiation sources, theprocessing device 120 may determine, based on the third image data ofthe subject corresponding to each of the plurality of radiation sources,target image data of the subject. In some embodiments, the processingdevice 120 may perform a three-dimensional (3D) reconstruction operationon the third image data of the subject corresponding to the plurality ofradiation sources. In some embodiments, the third image data may includethird projection data. The processing device 120 may reconstruct one ormore 2D third images based on the third projection data using a 2Dreconstruction technique. In some embodiments, the third image data mayinclude one or more reconstructed 2D third images. The processing device120 may perform the 3D reconstruction operation on the 2D third imagesto obtain the target image data (i.e., a 3D target image). The 3Dreconstruction operation may be performed by the processing device 120using a multiplanar reconstruction (MPR) technique, a maximum intensityprojection (MIP) technique, a surface shaded display (SSD) technique, avolume roaming technique (VRT), a curved planar reconstruction (CPR)technique, or the like, or a combination thereof. In some embodiments,the processing device 120 may perform a 3D reconstruction operation onthe third projection data of the subject corresponding to the pluralityof radiation sources using the calibration model as described elsewherein the present disclosure.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations and modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure. In someembodiments, one or more operations may be omitted and/or one or moreadditional operations may be added. For example, operation 1610 andoperation 1620 may be combined into a single operation. As anotherexample, one or more other optional operations (e.g., a storingoperation) may be added elsewhere in the process 1600. In the storingoperation, the processing device 120 may store information and/or data(e.g., the first image data of the subject, the second image data of thesubject, the third image data of the subject, the fourth image data ofthe subject, the scatter distribution, the estimated scannerdistribution, etc.) associated with the medical system 100 in a storagedevice (e.g., the storage device 130) disclosed elsewhere in the presentdisclosure. As still another example, before performing operation 1610to operation 1640, the processing device 120 may obtain the treatmentplanning and determine the first radiation parameter and the secondradiation parameter.

FIG. 17 is a block diagram illustrating an exemplary processing deviceaccording to some embodiments of the present disclosure. In someembodiments, processing device 120 may be implemented on a computingdevice 500 (e.g., the processor 510) illustrated in FIG. 5 or a CPU 640as illustrated in FIG. 6 . As illustrated in FIG. 7 , the processingdevice 120 may include a first acquisition module 1710, a secondacquisition module 1720, and a scatter correction module 1730. Each ofthe modules described above may be a hardware circuit that is designedto perform certain actions, e.g., according to a set of instructionsstored in one or more storage media, and/or any combination of thehardware circuit and the one or more storage media.

The first acquisition module 1710 may be configured to obtain image dataof a subject acquired by an imaging device via scanning the subjectbased on radiation beams emitted by the radiation source for one of aplurality of radiation sources. The image data may include scatter datacaused by a scattering of at least a portion of the radiation beamspassing through the subject. The image data may be the same as orsimilar to the second image data or the fourth image data as describedin FIG. 16 . More descriptions regarding obtaining the image data of thesubject may be found in FIG. 16 and the descriptions thereof.

The second acquisition module 1720 may be configured to obtain a trainedmachine learning model for one of the plurality of radiation sources. Insome embodiments, the trained machine learning model may be a process oran algorithm that is configured to processing the image data of thesubject. In some embodiments, the trained machine learning model mayinclude a convolutional neural network (CNN) model, a generativeadversarial network (GAN) model, or any other suitable type of model.Exemplary CNN models may include a Fully Convolutional Network, such asa V-NET model, a U-NET model, etc. Exemplary GAN models may include apix2pix model, a Wasserstein GAN (WGAN) model, a circle GAN model, etc.In some embodiments, the second acquisition module 1720 may obtain thetrained machine learning model from one or more components of theimaging system 100 (e.g., the storage device 130, the terminals(s) 140)or an external source via a network (e.g., the network 150). In someembodiments, the trained machine learning model may be generatedaccording to a machine learning algorithm. In some embodiments, thetrained machine learning model may be generated by a computing device(e.g., the processing device 120) by performing a process (e.g., process2000) for generating a trained machine learning model disclosed herein.More descriptions regarding the generation of the trained machinelearning model may be found in FIG. 20 and the descriptions thereof.

The scatter correction module 1730 may be configured to determine, basedon the trained machine learning model and the image data, correctedimage data of the subject corresponding to the radiation source for oneof a plurality of radiation sources. The corrected image data mayinclude an image quality higher than an image quality of the image datacaused by the scatter data (i.e., the scatter distribution) included inthe image data. That is, the corrected image data may be the image dataof the subject after scatter correction. In some embodiments, thescatter correction module 1730 may determine a scatter distributionassociated with the subject by inputting the image data into the trainedmachine learning model. The scatter correction module 1730 maydetermine, based on the scatter distribution and the image data, thecorrected image data. In some embodiments, the scatter correction module1730 may determine the corrected image data by inputting the image datainto the trained machine learning model.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. Apparently, for persons having ordinary skills inthe art, multiple variations and modifications may be conducted underthe teachings of the present disclosure. However, those variations andmodifications do not depart from the scope of the present disclosure.For example, the first acquisition module 1710 and the secondacquisition module 1720 may be integrated into one single module.

FIG. 18 is a schematic diagram illustrating an exemplary process fordetermining target image data of a subject according to some embodimentsof the present disclosure. In some embodiments, process 1800 may beimplemented as a set of instructions (e.g., an application) stored inthe storage device 130, storage 520, or storage 690. The processingdevice 120, the processor 510, and/or the CPU 640 may execute the set ofinstructions, and when executing the instructions, the processing device120, the processor 510, and/or the CPU 640 may be configured to performthe process 1800. The operations of the illustrated process presentedbelow are intended to be illustrative. In some embodiments, the process1800 may be accomplished with one or more additional operations notdescribed and/or without one or more of the operations discussed.Additionally, the order of the operations of the process 1800illustrated in FIG. 18 and described below is not intended to belimiting.

In 1810, for one of a plurality of radiation sources, the processingdevice 120 (e.g., the first acquisition module 1710) may obtain imagedata of a subject acquired by an imaging device via scanning the subjectbased on radiation beams emitted by the radiation source. The image datamay include scatter data caused by a scattering of at least a portion ofthe radiation beams passing through the subject.

For example, for one of the plurality of radiation sources configuredwith a beam stop array, the image data of the subject may be obtained bythe imaging device when the beam stop array is not arranged on the pathof radiation beams emitted by the radiation source. As another example,for one of the plurality of radiation sources not configured with a beamstop array, the image data of the subject may be obtained by the imagingdevice via scanning the subject. The image data may be the same as orsimilar to the second image data or the fourth image data as describedin FIG. 16 . More descriptions regarding obtaining the image data of thesubject may be found in FIG. 16 and the descriptions thereof.

In 1820, for one of the plurality of radiation sources, the processingdevice 120 (e.g., the second acquisition module 1720) may obtain atrained machine learning model.

In some embodiments, the trained machine learning model may be a processor an algorithm that is configured to processing the image data of thesubject. In some embodiments, the trained machine learning model mayinclude a convolutional neural network (CNN) model, a generativeadversarial network (GAN) model, or any other suitable type of model.Exemplary CNN models may include a Fully Convolutional Network, such asa V-NET model, a U-NET model, etc. Exemplary GAN models may include apix2pix model, a Wasserstein GAN (WGAN) model, a circle GAN model, etc.

In some embodiments, the processing device 120 (e.g., the sixthobtaining module 1820) may obtain the trained machine learning modelfrom one or more components of the imaging system 100 (e.g., the storagedevice 130, the terminals(s) 140) or an external source via a network(e.g., the network 150). For example, the trained machine learning modelmay be previously generated by a computing device (e.g., the processingdevice 120 or a processing device that is different from the processingdevice 120), and stored in a storage device (e.g., the storage device130, the storage 520, and/or the storage 690) of the imaging system 100.The processing device 120 may access the storage device and retrieve thetrained machine learning model. In some embodiments, the trained machinelearning model may be generated according to a machine learningalgorithm. The machine learning algorithm may include an artificialneural network algorithm, a deep learning algorithm, a decision treealgorithm, an association rule algorithm, an inductive logic programmingalgorithm, a support vector machine algorithm, a clustering algorithm, aBayesian network algorithm, a reinforcement learning algorithm, arepresentation learning algorithm, a similarity and metric learningalgorithm, a sparse dictionary learning algorithm, a genetic algorithm,a rule-based machine learning algorithm, or the like, or any combinationthereof. The machine learning algorithm used to generate the trainedmachine learning model may be a supervised learning algorithm, asemi-supervised learning algorithm, an unsupervised learning algorithm,etc. In some embodiments, the trained machine learning model may begenerated by a computing device (e.g., the processing device 120) byperforming a process (e.g., process 2000) for generating a trainedmachine learning model disclosed herein. More descriptions regarding thegeneration of the trained machine learning model may be found in FIG. 20and the descriptions thereof.

In 1830, for one of a plurality of radiation sources, the processingdevice 120 (e.g., the scatter correction module 1730) may determine,based on the trained machine learning model and the image data,corrected image data of the subject corresponding to the radiationsource.

The corrected image data may include an image quality higher than animage quality of the image data caused by the scatter data (i.e., thescatter distribution) included in the image data. That is, the correctedimage data may be the image data of the subject after scattercorrection.

In some embodiments, the processing device 120 (e.g., the scattercorrection module 1730) may determine a scatter distribution associatedwith the subject by inputting the image data into the trained machinelearning model. For example, the processing device 120 may input theimage data of the subject into the trained machine learning model. Anoutput result may be generated by the trained machine learning model.The output result of the trained machine learning model may include thescatter distribution associated with the subject. The processing device120 may determine, based on the scatter distribution and the image data,the corrected image data. For example, the corrected image data may bedetermined by subtracting the scatter distribution associated with thesubject from the image data of the subject.

In some embodiments, the processing device 120 (e.g., the scattercorrection module 1730) may determine the corrected image data byinputting the image data into the trained machine learning model. Forexample, the processing device 120 may input the image data of thesubject into the trained machine learning model. An output result may begenerated by the trained machine learning model. The output result ofthe trained machine learning model may include the corrected image dataof the subject.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations and modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure. In someembodiments, one or more operations may be omitted and/or one or moreadditional operations may be added. For example, operation 1810 andoperation 1820 may be combined into a single operation. As anotherexample, one or more other optional operations (e.g., a storingoperation) may be added elsewhere in the process 1800. In the storingoperation, the processing device 120 may store information and/or data(e.g., the image data of the subject, the scatter distribution, thecorrected image data, etc.) associated with the medical system 100 in astorage device (e.g., the storage device 130) disclosed elsewhere in thepresent disclosure.

FIG. 19 is a block diagram illustrating an exemplary processing deviceaccording to some embodiments of the present disclosure. In someembodiments, processing device 120 may be implemented on a computingdevice 500 (e.g., the processor 510) illustrated in FIG. 5 or a CPU 640as illustrated in FIG. 6 . As illustrated in FIG. 19 , the processingdevice 120 may include an acquisition module 1910 and a model trainingmodule 1920. Each of the modules described above may be a hardwarecircuit that is designed to perform certain actions, e.g., according toa set of instructions stored in one or more storage media, and/or anycombination of the hardware circuit and the one or more storage media.

The acquisition module 1910 may be configured to obtain a plurality oftraining samples. In some embodiments, each of the plurality of trainingsamples may include image data of a sample subject including scatterdata and a reference scatter distribution included in the image data ofthe sample subject. The reference scatter distribution included in theimage data of the sample subject may be also referred to as a traininglabel. In some embodiments, each of the plurality of training samplesmay include image data of a sample subject including scatter data andcorrected image data (also referred to as reference image data) of thesample subject that has been performed scatter correction. The correctedimage data of the sample subject may be also referred to as a traininglabel. In some embodiments, the sample subject corresponding to one ormore of the plurality of training samples may be a subject as describedelsewhere in the present disclosure (e.g., FIGS. 1, 8, 16, 18 , and thedescriptions thereof). In some embodiments, the plurality of trainingsamples may correspond to one of the at least a portion of the pluralityof radiation sources. The plurality of training samples may be acquiredbased on the one of the at least a portion of the plurality of radiationsources. In some embodiments, the plurality of training samples maycorrespond to the at least a portion of the plurality of radiationsources. The plurality of training samples may be acquired based on theat least a portion of the plurality of radiation sources. In someembodiments, a training sample may be previously generated and stored ina storage device (e.g., the storage device 130, the storage 520, thestorage 690, or an external database). The acquisition module 1910 mayretrieve the training sample directly from the storage device. In someembodiments, at least a portion of a training sample may be generated bythe acquisition module 1910.

The model training module 1920 may be configured to train a preliminarymachine learning model via performing multiple iterations. The trainedmachine learning model may be generated by training the preliminarymachine learning model. Each iteration may include updating parametervalues of the preliminary machine learning model based on a differencebetween the reference scatter distribution and an estimated scatterdistribution generated by the preliminary machine learning model basedon the inputted image data. In the training of the preliminary machinelearning model, the model training module 1920 may iteratively updatethe parameter value(s) of the preliminary machine learning model basedon the plurality of training samples. The updating of the preliminarylearning parameters of the machine learning model may be also referredto as updating the preliminary machine learning model. For example, themodel training module 1920 may update the model parameter(s) of thepreliminary machine learning model by performing one or more iterationsuntil a termination condition is satisfied. The termination conditionmay indicate whether the preliminary machine learning model issufficiently trained. The termination condition may relate to a costfunction or an iteration count of the training process. In response to adetermination that the termination condition is satisfied, the modeltraining module 1920 may designate the machine learning model with theparameter values updated in the last iteration as the trained machinelearning model (e.g., the trained machine learning model). On the otherhand, in response to a determination that the termination condition isnot satisfied, the model training module 1920 may update at least someof the parameter values of the preliminary machine learning model basedon the assessment result. The model training module 1920 may perform thenext iteration until the termination condition is satisfied. In the nextiteration, the model training module 1920 may obtain multiple groups oftraining samples in another batch. After the termination condition issatisfied in a certain iteration, the preliminary machine learning modelin the certain iteration having the updated value(s) of the learningparameter(s) may be designated as the trained machine learning model(e.g., the trained machine learning model).

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. Apparently, for persons having ordinary skills inthe art, multiple variations and modifications may be conducted underthe teachings of the present disclosure. However, those variations andmodifications do not depart from the scope of the present disclosure.For example,

FIG. 20 is a schematic flowchart illustrating an exemplary trainingprocess of a trained machine learning model according to someembodiments of the present disclosure. In some embodiments, process 2000may be implemented as a set of instructions (e.g., an application)stored in the storage device 130, storage 520, or storage 690. Theprocessing device 120, the processor 510, and/or the CPU 640 may executethe set of instructions, and when executing the instructions, theprocessing device 120, the processor 510, and/or the CPU 640 may beconfigured to perform the process 2000. The operations of theillustrated process presented below are intended to be illustrative. Insome embodiments, the process 2000 may be accomplished with one or moreadditional operations not described and/or without one or more of theoperations discussed. Additionally, the order of the operations ofprocess 2000 illustrated in FIG. 20 and described below is not intendedto be limiting. In some embodiments, a training process of the trainedmachine learning model as described in connection with operations 1820in FIG. 18 may be performed according to the process 2000.

In 2010, the processing device 120 (e.g., the acquisition module 1910)may obtain a plurality of training samples.

In some embodiments, each of the plurality of training samples mayinclude image data of a sample subject including scatter data and areference scatter distribution included in the image data of the samplesubject. The reference scatter distribution included in the image dataof the sample subject may be also referred to as a training label.

In some embodiments, each of the plurality of training samples mayinclude image data of a sample subject including scatter data andcorrected image data (also referred to as reference image data) of thesample subject that has been performed scatter correction. The correctedimage data of the sample subject may be also referred to as a traininglabel.

In some embodiments, the sample subject corresponding to one or more ofthe plurality of training samples may be a subject as describedelsewhere in the present disclosure (e.g., FIGS. 1, 8, 16, 18 , and thedescriptions thereof). In some embodiments, the plurality of trainingsamples may correspond to one of the at least a portion of the pluralityof radiation sources. The plurality of training samples may be acquiredbased on the one of the at least a portion of the plurality of radiationsources. For example, the image data in each of the plurality oftraining samples may be acquired by the one of the at least a portion ofthe plurality of radiation sources emitting radiation beams to scan thesample subject. That is, each of the at least a portion of the pluralityof radiation sources may correspond to a trained machine learning model.In some embodiments, the plurality of training samples may correspond tothe at least a portion of the plurality of radiation sources. Theplurality of training samples may be acquired based on the at least aportion of the plurality of radiation sources. For example, the imagedata in the plurality of training samples may be acquired by the atleast a portion of the plurality of radiation sources emitting radiationbeams to scan the sample subject. That is, the trained machine learningmodel may be applied to any of the at least a portion of the pluralityof radiation sources.

In some embodiments, a training sample may be previously generated andstored in a storage device (e.g., the storage device 130, the storage520, the storage 690, or an external database). The processing device120 may retrieve the training sample directly from the storage device.In some embodiments, at least a portion of a training sample may begenerated by the processing device 120. For example, for one of the atleast a portion of the plurality of radiation sources, the processingdevice 120 may obtain the image data (e.g., the second image data or thefourth image data as described in FIG. 16 ) of the sample subjectacquired by the imaging device when the beam stop array is not arrangedon a path of radiation beams emitted by the radiation source, and obtainfirst image data of the sample subject acquired by the imaging devicewhen the beam stop array is arranged on a path of radiation beamsemitted by the radiation source. The processing device 120 maydetermine, based on the image data (e.g., the second image data or thefourth image data as described in FIG. 16 ) of the sample subject andthe first image data, the reference scatter distribution associated withthe sample subject included in the image data of the sample subject. Asanother example, for one of the at least a portion of the plurality ofradiation sources, the processing device 120 may obtain the image dataof the sample subject acquired by the imaging device when the beam stoparray is not arranged on a path of radiation beams emitted by theradiation source, and obtain first image data of the sample subjectacquired by the imaging device when the beam stop array is arranged on apath of radiation beams emitted by the radiation source. Additionally oralternatively, the processing device 120 may determine, based on theimage data of the sample subject and the first image data, the correctedimage data of the sample subject.

In 2020, the processing device 120 (e.g., the model training module1920) may train a preliminary machine learning model via performingmultiple iterations.

The trained machine learning model may be generated by training thepreliminary machine learning model. In some embodiments, the preliminarymachine learning model to be trained may include a deep learning model(e.g., a convolutional neural network (CNN) model, a deep belief nets(DBN) machine learning model, a stacked auto-encoder network), arecurrent neural network (RNN) model, a long short term memory (LSTM)network model, a fully convolutional neural network (FCN) model, agenerative adversarial network (GAN) model, a backpropagation (BP)machine learning model, a radial basis function (RBF) machine learningmodel, an Elman machine learning model, or the like, or any combinationthereof. It should be noted that the descriptions of the machinelearning model in the present disclosure are merely provided forillustration, and not intended to limit the scope of the presentdisclosure. In some embodiments, the preliminary machine learning modelmay include a multi-layer structure. For example, the preliminarymachine learning model may include an input layer, an output layer, andone or more hidden layers between the input layer and the output layer.In some embodiments, the hidden layers may include one or moreconvolution layers, one or more rectified-linear unit layers (ReLUlayers), one or more pooling layers, one or more fully connected layers,or the like, or any combination thereof. As used herein, a layer of amodel may refer to an algorithm or a function for processing input dataof the layer. Different layers may perform different kinds of processingon their respective input. A successive layer may use output data from aprevious layer of the successive layer as input data. In someembodiments, the convolutional layer may include a plurality of kernels,which may be used to extract a feature. In some embodiments, each kernelof the plurality of kernels may filter a portion (i.e., a region). Thepooling layer may take an output of the convolutional layer as an input.The pooling layer may include a plurality of pooling nodes, which may beused to sample the output of the convolutional layer, so as to reducethe computational load of data processing and accelerate the speed ofdata processing speed. In some embodiments, the size of the matrixrepresenting the inputted data may be reduced in the pooling layer. Thefully connected layer may include a plurality of neurons. The neuronsmay be connected to the pooling nodes in the pooling layer. In the fullyconnected layer, a plurality of vectors corresponding to the pluralityof pooling nodes may be determined based on a training sample, and aplurality of weighting coefficients may be assigned to the plurality ofvectors. The output layer may determine an output based on the vectorsand the weighting coefficients obtained from the fully connected layer.

In some embodiments, each of the layers may include one or more nodes.In some embodiments, each node may be connected to one or more nodes ina previous layer. The number of nodes in each layer may be the same ordifferent. In some embodiments, each node may correspond to anactivation function. As used herein, an activation function of a nodemay define an output of the node given input or a set of inputs. In someembodiments, each connection between two of the plurality of nodes inthe preliminary machine learning model may transmit a signal from onenode to another node. In some embodiments, each connection maycorrespond to a weight. As used herein, a weight corresponding to aconnection may be used to increase or decrease the strength or impact ofthe signal at the connection.

The preliminary machine learning model may include a plurality ofparameters, such as architecture parameters, learning parameters, etc.Exemplary architecture parameters of the machine learning model mayinclude the size of a kernel of a layer, the total count (or number) oflayers, the count (or number) of nodes in each layer, a learning rate, abatch size, an epoch, etc. Exemplary learning parameters may include aconnected weight between two connected nodes, a bias vector relating toa node, etc.). Before the training, the preliminary machine learningmodel may have one or more initial parameter values. In the training ofthe preliminary machine learning model, learning parameters of thepreliminary machine learning model may be updated. Before the updatingprocess, values of the learning parameters of the preliminary machinelearning model may be initialized. For example, the connected weightsand/or the bias vector of nodes of the preliminary machine learningmodel may be initialized by assigning random values in a range, e.g.,the range from −1 to 1. As another example, all the connected weights ofthe preliminary machine learning model may be assigned the same value inthe range from −1 to 1, for example, 0. As still an example, the biasvector of nodes in the preliminary machine learning model may beinitialized by assigning random values in a range from 0 to 1. In someembodiments, the parameters of the preliminary machine learning modelmay be initialized based on a Gaussian random algorithm, a Xavieralgorithm, etc.

Each iteration may include updating parameter values of the preliminarymachine learning model based on a difference between the referencescatter distribution and an estimated scatter distribution generated bythe preliminary machine learning model based on the inputted image data.

In the training of the preliminary machine learning model, theprocessing device 120 may iteratively update the parameter value(s) ofthe preliminary machine learning model based on the plurality oftraining samples. The updating of the preliminary learning parameters ofthe machine learning model may be also referred to as updating thepreliminary machine learning model. For example, the processing device120 may update the model parameter(s) of the preliminary machinelearning model by performing one or more iterations until a terminationcondition is satisfied. The termination condition may indicate whetherthe preliminary machine learning model is sufficiently trained. Thetermination condition may relate to a cost function or an iterationcount of the training process. For example, the processing device 120may determine a loss function of the preliminary machine learning modeland determine a value of the cost function based on the differencebetween an estimated output and an actual output or desired output(i.e., reference output). Further, the processing device 120 maydetermine the termination condition is satisfied if the value of theloss function is less than a threshold. The threshold may be defaultsettings of the imaging system 100 or may be adjustable under differentsituations. As another example, the termination condition may besatisfied if the value of the cost function converges. The convergencemay be deemed to have occurred if the variation of the values of thecost function in two or more consecutive iterations is smaller than athreshold (e.g., a constant). As still another example, the processingdevice 120 may determine the termination condition is satisfied if aspecified number (or count) of iterations are performed in the trainingprocess. In response to a determination that the termination conditionis satisfied, the processing device 120 may designate the machinelearning model with the parameter values updated in the last iterationas the trained machine learning model (e.g., the trained machinelearning model). On the other hand, in response to a determination thatthe termination condition is not satisfied, the processing device 120may update at least some of the parameter values of the preliminarymachine learning model based on the assessment result. For example, theprocessing device 120 may update the value(s) of the learningparameter(s) of the preliminary machine learning model based on thevalue of the loss function according to, for example, a backpropagationalgorithm. The processing device 120 may perform the next iterationuntil the termination condition is satisfied. In the next iteration, theprocessing device 120 may obtain multiple groups of training samples inanother batch. The size of the batch may refer to a group count ornumber of the multiple groups of training samples. After the terminationcondition is satisfied in a certain iteration, the preliminary machinelearning model in the certain iteration having the updated value(s) ofthe learning parameter(s) may be designated as the trained machinelearning model (e.g., the trained machine learning model).

It should be noted that the above description is merely provided forillustration, and not intended to limit the scope of the presentdisclosure. For persons having ordinary skills in the art, multiplevariations and modifications may be made under the teachings of thepresent disclosure. However, those variations and modifications do notdepart from the scope of the present disclosure. In some embodiments,one or more operations may be omitted and/or one or more additionaloperations may be added. For example, one or more other optionaloperations (e.g., a storing operation) may be added elsewhere in theprocess 2000. In the storing operation, the processing device 120 maystore information and/or data (e.g., parameter values, etc.) associatedwith the training of the preliminary machine learning model in a storagedevice (e.g., the storage device 130) disclosed elsewhere in the presentdisclosure.

Having thus described the basic concepts, it may be rather apparent tothose skilled in the art after reading this detailed disclosure that theforegoing detailed disclosure is intended to be presented by way ofexample only and is not limiting. Various alterations, improvements, andmodifications may occur and are intended to those skilled in the art,though not expressly stated herein. These alterations, improvements, andmodifications are intended to be suggested by this disclosure and arewithin the spirit and scope of the exemplary embodiments of thisdisclosure.

Moreover, certain terminology has been used to describe embodiments ofthe present disclosure. For example, the terms “one embodiment,” “anembodiment,” and/or “some embodiments” mean that a particular feature,structure, or characteristic described in connection with the embodimentis included in at least one embodiment of the present disclosure.Therefore, it is emphasized and should be appreciated that two or morereferences to “an embodiment” or “one embodiment” or “an alternativeembodiment” in various portions of this specification are notnecessarily all referring to the same embodiment. Furthermore, theparticular features, structures, or characteristics may be combined assuitable in one or more embodiments of the present disclosure.

Further, it will be appreciated by one skilled in the art, aspects ofthe present disclosure may be illustrated and described herein in any ofa number of patentable classes or context including any new and usefulprocess, machine, manufacture, or composition of matter, or any new anduseful improvement thereof. Accordingly, aspects of the presentdisclosure may be implemented entirely hardware, entirely software(including firmware, resident software, micro-code, etc.) or combiningsoftware and hardware implementation that may all generally be referredto herein as a “unit,” “module,” or “system.” Furthermore, aspects ofthe present disclosure may take the form of a computer program productembodied in one or more computer-readable media having computer-readableprogram code embodied thereon.

A non-transitory computer-readable signal medium may include apropagated data signal with computer readable program code embodiedtherein, for example, in baseband or as part of a carrier wave. Such apropagated signal may take any of a variety of forms, includingelectromagnetic, optical, or the like, or any suitable combinationthereof. A computer-readable signal medium may be any computer-readablemedium that is not a computer-readable storage medium and that maycommunicate, propagate, or transport a program for use by or inconnection with an instruction execution system, apparatus, or device.Program code embodied on a computer-readable signal medium may betransmitted using any appropriate medium, including wireless, wireline,optical fiber cable, RF, or the like, or any suitable combination of theforegoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object-oriented programming languagesuch as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB. NET,Python, or the like, conventional procedural programming languages, suchas the “C” programming language, Visual Basic, Fortran, Perl, COBOL,PHP, ABAP, dynamic programming languages such as Python, Ruby, andGroovy, or other programming languages. The program code may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider) or in a cloud computing environment or offered as aservice such as a Software as a Service (SaaS).

Furthermore, the recited order of processing elements or sequences, orthe use of numbers, letters, or other designations, therefore, is notintended to limit the claimed processes and methods to any order exceptas may be specified in the claims. Although the above disclosurediscusses through various examples what is currently considered to be avariety of useful embodiments of the disclosure, it is to be understoodthat such detail is solely for that purpose and that the appended claimsare not limited to the disclosed embodiments, but, on the contrary, areintended to cover modifications and equivalent arrangements that arewithin the spirit and scope of the disclosed embodiments. For example,although the implementation of various components described above may beembodied in a hardware device, it may also be implemented as asoftware-only solution, e.g., an installation on an existing server ormobile device.

Similarly, it should be appreciated that in the foregoing description ofembodiments of the present disclosure, various features are sometimesgrouped together in a single embodiment, figure, or description thereofto streamline the disclosure aiding in the understanding of one or moreof the various inventive embodiments. This method of disclosure,however, is not to be interpreted as reflecting an intention that theclaimed object matter requires more features than are expressly recitedin each claim. Rather, inventive embodiments lie in less than allfeatures of a single foregoing disclosed embodiment.

In some embodiments, the numbers expressing quantities, properties, andso forth, used to describe and claim certain embodiments of theapplication are to be understood as being modified in some instances bythe term “about,” “approximate,” or “substantially.” For example,“about,” “approximate” or “substantially” may indicate ±20% variation ofthe value it describes, unless otherwise stated. Accordingly, in someembodiments, the numerical parameters set forth in the writtendescription and attached claims are approximations that may varydepending upon the desired properties sought to be obtained by aparticular embodiment. In some embodiments, the numerical parametersshould be construed in light of the number of reported significantdigits and by applying ordinary rounding techniques. Notwithstandingthat the numerical ranges and parameters setting forth the broad scopeof some embodiments of the application are approximations, the numericalvalues set forth in the specific examples are reported as precisely aspracticable.

Each of the patents, patent applications, publications of patentapplications, and other material, such as articles, books,specifications, publications, documents, things, and/or the like,referenced herein is hereby incorporated herein by this reference in itsentirety for all purposes, excepting any prosecution file historyassociated with same, any of same that is inconsistent with or inconflict with the present document, or any of same that may have alimiting effect as to the broadest scope of the claims now or laterassociated with the present document. By way of example, should there beany inconsistency or conflict between the description, definition,and/or the use of a term associated with any of the incorporatedmaterial and that associated with the present document, the description,definition, and/or the use of the term in the present document shallprevail.

In closing, it is to be understood that the embodiments of theapplication disclosed herein are illustrative of the principles of theembodiments of the application. Other modifications that may be employedmay be within the scope of the application. Thus, by way of example, butnot of limitation, alternative configurations of the embodiments of theapplication may be utilized in accordance with the teachings herein.Accordingly, embodiments of the present application are not limited tothat precisely as shown and described.

1. A system for imaging via an imaging device including a plurality ofradiation sources, each of at least a portion of the plurality ofradiation sources being configured with a beam stop array that isconfigured to block at least a portion of radiation beams emitted by theradiation source, the system comprising: at least one storage deviceincluding a set of instructions; and at least one processor incommunication with the at least one storage device, wherein whenexecuting the set of instructions, the at least one processor isdirected to perform operations including: for one of the at least aportion of the plurality of radiation sources, obtaining first imagedata of a subject acquired by the imaging device when the beam stoparray is arranged on a path of radiation beams emitted by the radiationsource; obtaining second image data of the subject acquired by theimaging device when the beam stop array is not arranged on the path ofradiation beams emitted by the radiation source; determining, based onthe first image data, a scatter distribution associated with the subjectincluded in the second image data; and determining, based on the scatterdistribution and the second image data, third image data of the subjectcorresponding to each of the at least a portion of the plurality ofradiation sources.
 2. (canceled)
 3. The system of claim 1, wherein thebeam stop array includes a support and multiple elements each of whichincludes a material with an attenuation coefficient exceeding anattenuation coefficient of a material of the support.
 4. The system ofclaim 1, wherein the determining, based on the first image data, ascatter distribution associated with the subject includes: performing aninterpolation operation on the first image data to obtain the scatterdistribution.
 5. The system of claim 1, wherein a radiation dose of thefirst image data is less than a radiation dose of the second image data.6. The system of claim 5, wherein the determining, based on the scatterdistribution and the second image data, third image data of the subjectcorresponding to each of the at least a portion of the plurality ofradiation sources radiation source includes: determining a ratio of theradiation dose of the second image data and the radiation dose of thefirst image data; and determining, based on the ratio, the scatterdistribution, and the second image data, the third image data.
 7. Thesystem of claim 1, wherein the plurality of radiation sources includes atarget portion in which each radiation source is not configured with abeam stop array, the operations further include: for a radiation sourcein the target portion, obtaining fourth image data of the subjectacquired by the imaging device via scanning the subject; determining anestimated scanner distribution included in the fourth image data basedon one or more scatter distributions that are determined based on thefirst image data corresponding to one or more reference radiationsources; and determining, based on the fourth image data and the scannerdistribution, third image data of the subject corresponding to theradiation source in the target portion.
 8. The system of claim 7,wherein the determining an estimated scanner distribution included inthe fourth image data includes: determining the estimated scannerdistribution included in the fourth image data by performing aninterpolation operation on the one or more scatter distributions.
 9. Thesystem of claim 7, wherein the operations further include: determining,based on the third image data of the subject corresponding to each ofthe plurality of radiation sources, target image data of the subject.10-11. (canceled)
 12. A system for imaging via an imaging deviceincluding a plurality of radiation sources and a detector, each of atleast a portion of the plurality of radiation sources being configuredwith a beam stop array that is configured to block at least a portion ofradiation beams emitted by the radiation source, the system comprising:at least one storage device including a set of instructions; and atleast one processor in communication with the at least one storagedevice, wherein when executing the set of instructions, the at least oneprocessor is directed to perform operations including: for one of theplurality of radiation sources, obtaining image data of the subjectacquired by the imaging device via scanning the subject based onradiation beams emitted by the radiation source, the image dataincluding scatter data caused by a scattering of at least a portion ofthe radiation beams passing through the subject; obtaining a trainedmachine learning model; determining, based on the trained machinelearning model and the image data, target image data of the subjectcorresponding to the radiation source, the target image data includingan image quality higher than an image quality of the image data causedby the scatter data included in the image data.
 13. The system of claim12, wherein the determining, based on the trained machine learning modeland the image data, target image data of the subject corresponding tothe radiation source includes: determining a scatter distributionassociated with the subject by inputting the image data into the trainedmachine learning model; and determining, based on the scatterdistribution and the image data, the target image data.
 14. The systemof claim 12, wherein the determining, based on the trained machinelearning model and the image data, target image data of the subjectcorresponding to the radiation source includes: determining the targetimage data by inputting the image data into the trained machine learningmodel.
 15. The system of claim 12, wherein the trained machine learningis provided by a process including: obtaining a plurality of trainingsamples each of which includes image data of a sample subject includingscatter data and a reference scatter distribution included in the imagedata of the sample subject; training a preliminary machine learningmodel via performing multiple iterations, each iteration includingupdating parameter values of the preliminary machine learning modelbased on a difference between the reference scatter distribution and anestimated scatter distribution generated by the preliminary machinelearning model based on the inputted image data.
 16. The system of claim15, wherein the obtaining a plurality of training samples including: forone of the at least a portion of the plurality of radiation sources,obtaining the image data of the sample subject acquired by the imagingdevice when the beam stop array is not arranged on a path of radiationbeams emitted by the radiation source; obtaining first image data of thesample subject acquired by the imaging device when the beam stop arrayis arranged on a path of radiation beams emitted by the radiationsource; determining, based on the first image data, the referencescatter distribution associated with the sample subject included in theimage data of the sample subject. 17-18. (canceled)
 19. A system forimaging via an imaging device including a plurality of radiation sourcesand a detector, the system comprising: for each of at least a portion ofthe plurality of radiation sources, obtaining image data of a subjectacquired by the imaging device via scanning the subject based onradiation beams emitted by the radiation source; obtaining a calibrationmodel corresponding to the radiation source, the calibration modelindicating a transform relationship between a position of each pixel inthe image data and a position of a portion of the subject represented bythe pixel in a space; and determining, based on the image data of thesubject, target image data of the subject using the calibration model.20. The system of claim 19, wherein the determining, based on the imagedata of the subject, target image data of the subject using thecalibration model includes: performing a three-dimensionalreconstruction operation on the image data corresponding to at least aportion of the plurality of radiation sources using multiple calibrationmodels each of which corresponds to one of at least a portion of theplurality of radiation sources.
 21. The system of claim 19, wherein thecalibration model is provided by a process including: obtaining imagedata of a reference object acquired by the imaging device scanning thereference object, the reference object including a support and multipleelements arranged on the support, each of the multiple elementsincluding a material with an attenuation coefficient being differentfrom an attenuation coefficient of a material of the support, the imagedata including representations of at least six elements among themultiple elements; determining a first position of each of the at leastsix elements in the image data; determining a second position of each ofthe at least six elements in a space where the imaging device isarranged; and determining, based on the first position and the secondposition, the calibration model.
 22. The system of claim 21, wherein thedetermining, based on the first position and the second position, thecalibration model includes: determining, based on the first position andthe second position, multiple pairs of positions each of which includesthe first position and the second position of a same element among theat least six elements; and determining, based on the multiple pairs ofpositions, the calibration model.
 23. The system of claim 21, whereinthe first positions of six elements in the at least six elements aredifferent.
 24. The system of claim 21, wherein one or more elementsamong the at least six elements are not overlapped on transmission pathsof the radiation beams emitted by the radiation source.
 25. The systemof claim 21, wherein an interval between two adjacent elements in the atleast six elements is determined based on at least one of a firstdistance between the radiation source or the reference object and asecond distance between the radiation source and the detector of theimaging device. 26-34. (canceled)